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University of Reading

Department of Computer Science

Programming in Python for Data Science using Twitter

Shavin Croos and Uthman Maigari

Module code: CS3PP19

Assignment report title: Programming in Python for Data Science using Twitter

Student Numbers: 27015244, 28017661

Date of completion: 28/11/2021

Actual time spent on the assignment (hours): 30 hours

Assignment evaluation (3 key points):

1) Not adequate information given on how to approach Task 3. Showing how to use NetworkX as a demo for carrying out Task 3 with an example Twitter account would have sufficed.

TASK 1¶

Influencer Data¶

import tweepy
import pandas as pd
import regex

api_key = 
api_secret_key = 
access_token = 
access_token_secret = 

auth = tweepy.OAuthHandler(api_key, api_secret_key)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
infl1 = api.get_user('katyperry')
infl2 = api.get_user('kingjames')
infl3 = api.get_user('cristiano')

created_at = infl1.created_at
created_at = infl2.created_at
created_at = infl3.created_at

print("FIRST INFLUENCER")
print("Influencer Name: " + infl1.screen_name)
print("Followers Count: " + str(infl1.followers_count))
print("Influencer ID: " + str(infl1.id))
print("Account was created on: " + str(infl1.created_at))
print('\n')

print("SECOND INFLUENCER")
print("Influencer Name: " + infl2.screen_name)
print("Followers Count: " + str(infl2.followers_count))
print("Influencer ID: " + str(infl2.id))
print("Account was created on: " + str(infl2.created_at))
print('\n')

print("THIRD INFLUENCER")
print("Influencer Name: " + infl3.screen_name)
print("Followers Count: " + str(infl3.followers_count))
print("Influencer ID: " + str(infl3.id))
print("Account was created on: " + str(infl3.created_at))

FIRST INFLUENCER
Influencer Name: katyperry
Followers Count: 108771123
Influencer ID: 21447363
Account was created on: 2009-02-20 23:45:56


SECOND INFLUENCER
Influencer Name: KingJames
Followers Count: 50472127
Influencer ID: 23083404
Account was created on: 2009-03-06 16:25:53


THIRD INFLUENCER
Influencer Name: Cristiano
Followers Count: 95833851
Influencer ID: 155659213
Account was created on: 2010-06-14 19:09:20

Influencer Tweet Data (Tablised)¶

Katy Perry (300 Tweets Pre-Cleaning)

number_of_tweets = 300
tweet_id = []
tweets = []
likes = []
retweets = []
time = []

for i in tweepy.Cursor(api.user_timeline, id = "katyperry", tweet_mode = "extended").items(number_of_tweets):
  tweet_id.append(i.id)
  tweets.append(i.full_text)
  likes.append(i.favorite_count)
  retweets.append(i.retweet_count)
  time.append(i.created_at)
  
pd.set_option('display.max_rows', 300)
pd.set_option('display.max_colwidth', None)
df1_prc = pd.DataFrame({'tweet_id':tweet_id, 'tweets':tweets, 'likes':likes, 'retweets': retweets, 'time':time})
left_algn_prc1 = df1_prc.style.set_properties(**{'text-align': 'left'})
left_algn_prc1 = left_algn_prc1.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_prc1)

LeBron James (300 Tweets Pre-Cleaning)

number_of_tweets = 300
tweet_id = []
tweets = []
likes = []
retweets = []
time = []

for i in tweepy.Cursor(api.user_timeline, id = "KingJames", tweet_mode = "extended").items(number_of_tweets):
  tweet_id.append(i.id)
  tweets.append(i.full_text)
  likes.append(i.favorite_count)
  retweets.append(i.retweet_count)
  time.append(i.created_at)

pd.set_option('display.max_rows', 300)
pd.set_option('display.max_colwidth', None)
df2_prc = pd.DataFrame({'tweet_id':tweet_id, 'tweets':tweets, 'likes':likes, 'retweets': retweets, 'time':time})
left_algn_prc2 = df2_prc.style.set_properties(**{'text-align': 'left'})
left_algn_prc2 = left_algn_prc2.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_prc2)

Cristiano Ronaldo (300 Tweets Pre-Cleaning)

number_of_tweets = 300
tweet_id = []
tweets = []
likes = []
retweets = []
time = []

for i in tweepy.Cursor(api.user_timeline, id = "Cristiano", tweet_mode = "extended").items(number_of_tweets):
  tweet_id.append(i.id)
  tweets.append(i.full_text)
  likes.append(i.favorite_count)
  retweets.append(i.retweet_count)
  time.append(i.created_at)
  
pd.set_option('display.max_rows', 300)
pd.set_option('display.max_colwidth', -1)
df3_prc = pd.DataFrame({'tweet_id':tweet_id, 'tweets':tweets, 'likes':likes, 'retweets':retweets, 'time':time})
left_algn_prc3 = df3_prc.style.set_properties(**{'text-align': 'left'})
left_algn_prc3 = left_algn_prc3.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_prc3)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  app.launch_new_instance()

Katy Perry (300 Tweets Post-Cleaning)

pd.set_option('display.max_rows', 300)
pd.set_option('display.max_colwidth', None)
df1_poc = df1_prc[~df1_prc.tweets.str.contains("RT")]

def cleanTxt(text):
  text = regex.sub(r'#', '', text)
  text = regex.sub(r'https?:\/\/\S+', '', text)
  text = regex.sub(r'\n', '', text)
  return text

df1_poc['tweets'] = df1_poc['tweets'].apply(cleanTxt)
df1_poc = df1_poc.reset_index(drop = True)
left_algn_poc1 = df1_poc.style.set_properties(**{'text-align': 'left'})
left_algn_poc1 = left_algn_poc1.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_poc1)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  # This is added back by InteractiveShellApp.init_path()

LeBron James (300 Tweets Post-Cleaning)

pd.set_option('display.max_rows', 300)
pd.set_option('display.max_colwidth', None)
df2_poc = df2_prc[~df2_prc.tweets.str.contains("RT")]

def cleanTxt(text):
  text = regex.sub(r'#', '', text)
  text = regex.sub(r'https?:\/\/\S+', '', text)
  text = regex.sub(r'\n', '', text)
  return text

df2_poc['tweets'] = df2_poc['tweets'].apply(cleanTxt)
df2_poc = df2_poc.reset_index(drop = True)
left_algn_poc2 = df2_poc.style.set_properties(**{'text-align': 'left'})
left_algn_poc2 = left_algn_poc2.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_poc2)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  # This is added back by InteractiveShellApp.init_path()

Cristiano Ronaldo (300 Tweets Post-Cleaning)

pd.set_option('display.max_rows', 300)
pd.set_option('display.max_colwidth', None)
df3_poc = df3_prc[~df3_prc.tweets.str.contains("RT")]

def cleanTxt(text):
  text = regex.sub(r'#', '', text)
  text = regex.sub(r'https?:\/\/\S+', '', text)
  text = regex.sub(r'\n', '', text)
  return text

df3_poc['tweets'] = df3_poc['tweets'].apply(cleanTxt)
df3_poc = df3_poc.reset_index(drop = True)
left_algn_poc3 = df3_poc.style.set_properties(**{'text-align': 'left'})
left_algn_poc3 = left_algn_poc3.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_poc3)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  # This is added back by InteractiveShellApp.init_path()

Firstly we needed access to the API connection which has the Keys and Access Tokens, then importing the necessary python libraries. To extract information of the influencers we identify the user ID of the profile, get the user object of the profile using the get_user(), followers_count method and the user ID then extracting the screen_name attribute. We extracted each of the influencers 300 tweets using a tweepy.cursor function which searches twitter for tweets containing the search term, the .cursor() returns an object that you can iterate or loop over to access the data collected. Each item in the iterator has various attributes which are: the text of the tweet, the date the tweet was sent, the number of likes of the tweet, the number of retweets of the tweet and the ID of the tweet as well.

After all 300 tweets were extracted from each influencer in form of a pandas dataframe we performed a data cleansing to remove all the retweets of each influencers tweets because the tweets may contain duplicate content that might skew our analysis, we ignore all tweets by adding -filter:retweets or ~df3_prc.tweets.str.contains("RT") to our query. Another form of data cleansing we did was to remove unwanted hypertext links, hash tags and any '\n's to make the data frame a lot clearer to comprehend.

Some lessons learned while studying these tasks were: the API is clean and comes with rich developer tools, the data is rich in information and has a data format fit for analysis, and learned new attributes which helped in the extraction of more useful data. Also this task was about researching and mining twitter data and how to identify cleansing the data for pre-processing, the use of a pandas data frame was archived which helped in cleansing the data.

Some of our findings were good, so the influencer “Katy Perry” had the following which was 108,769,417 and our second influencer “King James” had the most Retweets amongst them.

TASK 2¶

Understanding The Data¶

df1_prc.shape

(300, 5)

df2_prc.shape

(300, 5)

df3_prc.shape

(300, 5)

df1_poc.shape

(294, 5)

df2_poc.shape

(210, 5)

df3_poc.shape

(299, 5)

kpstatsprc = df1_prc.describe()
kpstatsprc.drop(['tweet_id'], axis = 1)

kpstatspoc = df1_poc.describe()
kpstatspoc.drop(['tweet_id'], axis = 1)

ljstatsprc = df2_prc.describe()
ljstatsprc.drop(['tweet_id'], axis = 1)

ljstatspoc = df2_poc.describe()
ljstatspoc.drop(['tweet_id'], axis = 1)

crstatsprc = df3_prc.describe()
crstatsprc.drop(['tweet_id'], axis = 1)

crstatspoc = df3_poc.describe()
crstatspoc.drop(['tweet_id'], axis = 1)

df1_prc.columns

Index(['tweet_id', 'tweets', 'likes', 'retweets', 'time'], dtype='object')

df1_poc.columns

Index(['tweet_id', 'tweets', 'likes', 'retweets', 'time'], dtype='object')

df2_prc.columns

Index(['tweet_id', 'tweets', 'likes', 'retweets', 'time'], dtype='object')

df2_poc.columns

Index(['tweet_id', 'tweets', 'likes', 'retweets', 'time'], dtype='object')

df3_prc.columns

Index(['tweet_id', 'tweets', 'likes', 'retweets', 'time'], dtype='object')

df3_poc.columns

Index(['tweet_id', 'tweets', 'likes', 'retweets', 'time'], dtype='object')

df1_prc.nunique()

tweet_id    300
tweets      300
likes       290
retweets    237
time        300
dtype: int64

df1_poc.nunique()

tweet_id    294
tweets      290
likes       286
retweets    231
time        294
dtype: int64

df2_prc.nunique()

tweet_id    300
tweets      300
likes       210
retweets    266
time        300
dtype: int64

df2_poc.nunique()

tweet_id    210
tweets      209
likes       209
retweets    195
time        210
dtype: int64

df3_prc.nunique()

tweet_id    300
tweets      300
likes       300
retweets    298
time        300
dtype: int64

df3_poc.nunique()

tweet_id    299
tweets      299
likes       299
retweets    297
time        299
dtype: int64

Cleansing The Data Further¶

df1_prc.isnull().sum()

tweet_id    0
tweets      0
likes       0
retweets    0
time        0
dtype: int64

df1_poc.isnull().sum()

tweet_id    0
tweets      0
likes       0
retweets    0
time        0
dtype: int64

df2_prc.isnull().sum()

tweet_id    0
tweets      0
likes       0
retweets    0
time        0
dtype: int64

df2_poc.isnull().sum()

tweet_id    0
tweets      0
likes       0
retweets    0
time        0
dtype: int64

df3_prc.isnull().sum()

tweet_id    0
tweets      0
likes       0
retweets    0
time        0
dtype: int64

df3_poc.isnull().sum()

tweet_id    0
tweets      0
likes       0
retweets    0
time        0
dtype: int64

kp_tweet_content = df1_poc.drop(['tweet_id'], axis = 1)
kp_tweet_content.head()

lj_tweet_content = df2_poc.drop(['tweet_id'], axis = 1)
lj_tweet_content.head()

cr_tweet_content = df3_poc.drop(['tweet_id'], axis = 1)
cr_tweet_content.head()

Analysing The Data¶

print('Katy Perry Total Likes (Pre-Clean):')
df1_prc['likes'].sum()

Katy Perry Total Likes (Pre-Clean):

3461684

print('Katy Perry Total Likes (Post-Clean):')
df1_poc['likes'].sum()

Katy Perry Total Likes (Post-Clean):

3414965

print('LeBron James Total Likes (Pre-Clean):')
df2_prc['likes'].sum()

LeBron James Total Likes (Pre-Clean):

6338959

print('LeBron James Total Likes (Post-Clean):')
df2_poc['likes'].sum()

LeBron James Total Likes (Post-Clean):

6338959

print('Cristiano Ronaldo Total Likes (Pre-Clean):')
df3_prc['likes'].sum()

Cristiano Ronaldo Total Likes (Pre-Clean):

67119376

print('Cristiano Ronaldo Total Likes (Post-Clean):')
df3_poc['likes'].sum()

Cristiano Ronaldo Total Likes (Post-Clean):

67040737

print('Katy Perry Total Retweets (Pre-Clean):')
df1_prc['retweets'].sum()

Katy Perry Total Retweets (Pre-Clean):

392586

print('Katy Perry Total Retweets (Post-Clean):')
df1_poc['retweets'].sum()

Katy Perry Total Retweets (Post-Clean):

385373

print('LeBron James Total Retweets (Pre-Clean):')
df2_prc['retweets'].sum()

LeBron James Total Retweets (Pre-Clean):

528878

print('LeBron James Total Retweets (Post-Clean):')
df2_poc['retweets'].sum()

LeBron James Total Retweets (Post-Clean):

484496

print('Cristiano Ronaldo Total Retweets (Pre-Clean):')
df3_prc['retweets'].sum()

Cristiano Ronaldo Total Retweets (Pre-Clean):

4193661

print('Cristiano Ronaldo Total Retweets (Post-Clean):')
df3_poc['retweets'].sum()

Cristiano Ronaldo Total Retweets (Post-Clean):

4189002

As shown above, Cristiano Ronaldo has the most number of Twitter likes in all 300 of his tweets and Katy Perry has the least number of Twitter likes in all 300 of her tweets.

kp_tweet_content_prc = df1_prc.sort_values(by='likes', ascending=False)
kp_tweet_content_prc = kp_tweet_content_prc.reset_index(drop=True)
kp_tweet_content_prc.head()

kp_tweet_content_poc = df1_poc.sort_values(by='likes', ascending=False)
kp_tweet_content_poc = kp_tweet_content_poc.reset_index(drop=True)
kp_tweet_content_poc.head()

lj_tweet_content_prc = df2_prc.sort_values(by='likes', ascending=False)
lj_tweet_content_prc = lj_tweet_content_prc.reset_index(drop=True)
lj_tweet_content_prc.head()

lj_tweet_content_poc = df2_poc.sort_values(by='likes', ascending=False)
lj_tweet_content_poc = lj_tweet_content_poc.reset_index(drop=True)
lj_tweet_content_poc.head()

cr_tweet_content_prc = df3_prc.sort_values(by='likes', ascending=False)
cr_tweet_content_prc = cr_tweet_content_prc.reset_index(drop=True)
cr_tweet_content_prc.head()

cr_tweet_content_poc = df3_poc.sort_values(by='likes', ascending=False)
cr_tweet_content_poc = cr_tweet_content_poc.reset_index(drop=True)
cr_tweet_content_poc.head()

statuses_count_1 = infl1.statuses_count
statuses_count_2 = infl2.statuses_count
statuses_count_3 = infl3.statuses_count

print("The total number of Katy Perry tweets are: " + str(statuses_count_1))
print("The total number of Lebron James tweets are: " + str(statuses_count_2))
print("The total number of Cristiano Ronaldo tweets are: " + str(statuses_count_3))

The total number of Katy Perry tweets are: 11464
The total number of Lebron James tweets are: 8171
The total number of Cristiano Ronaldo tweets are: 3736

import matplotlib.pyplot as plt
import numpy as np

infl_best = pd.DataFrame([["Katy Perry", df1_poc['likes'].sum(), infl1.statuses_count], ["Lebron James", df2_poc['likes'].sum(), infl2.statuses_count], ["Cristiano Ronaldo", df3_poc['likes'].sum(), infl3.statuses_count]], columns = ["Influencer", "Total Likes", "Total Tweets"])
infl_best.set_index('Influencer')

fig = plt.figure(figsize=(6,5))

x = np.arange(3)
ax = fig.add_subplot(111)
ax2 = ax.twinx()

width = 0.4

infl_best['Total Likes'].plot(kind = 'bar', color = 'red', ax=ax, width=width, position = -0.5)
infl_best['Total Tweets'].plot(kind = 'bar', color = 'blue', ax=ax2, width=width)

ax.set_ylabel('Total Likes (1 × 10^7)', fontsize=14)
ax.set_xlabel('Influencers', fontsize=14)
ax2.set_ylabel('Total Tweets (1 × 10^3)', fontsize=14)

plt.xticks(x + width/2, infl_best['Influencer'])
plt.xlim([-0.35,2.75])
plt.show()

print('\nNote: blue represents the Total Tweets and red represents the Total Likes. \nCould not show the key on the graph as it would cover the graph and will make seeing patterns difficult.')

Note: blue represents the Total Tweets and red represents the Total Likes. 
Could not show the key on the graph as it would cover the graph and will make seeing patterns difficult.

import matplotlib.pyplot as plt
import numpy as np

infl_follower_count = pd.DataFrame([["Katy Perry", infl1.followers_count], ["Lebron James", infl2.followers_count], ["Cristiano Ronaldo", infl3.followers_count]], columns = ["Influencer", "Follower Count"])
infl_follower_count.set_index('Influencer')

fig = plt.figure()

x = np.arange(3)
ax = fig.add_subplot(111)

infl_follower_count['Follower Count'].plot(kind = 'bar', color = 'green', ax=ax, width=width, position = 0)

ax.set_ylabel('Follower Count', fontsize=14)
ax.set_xlabel('Influencers', fontsize=14)

plt.xticks(x + width/2, infl_best['Influencer'])
plt.xlim([-0.35,2.75])
plt.legend()
plt.show()

kp_tweet_content_prc = df1_prc.sort_values(by='retweets', ascending=False)
kp_tweet_content_prc = kp_tweet_content_prc.reset_index(drop=True)
kp_tweet_content_prc.head()

kp_tweet_content_poc = df1_poc.sort_values(by='retweets', ascending=False)
kp_tweet_content_poc = kp_tweet_content_poc.reset_index(drop=True)
kp_tweet_content_poc.head()

lj_tweet_content_prc = df2_prc.sort_values(by='retweets', ascending=False)
lj_tweet_content_prc = lj_tweet_content_prc.reset_index(drop=True)
lj_tweet_content_prc.head()

lj_tweet_content_poc = df2_poc.sort_values(by='retweets', ascending=False)
lj_tweet_content_poc = lj_tweet_content_poc.reset_index(drop=True)
lj_tweet_content_poc.head()

cr_tweet_content_prc = df3_prc.sort_values(by='retweets', ascending=False)
cr_tweet_content_prc = cr_tweet_content_prc.reset_index(drop=True)
cr_tweet_content_prc.head()

cr_tweet_content_poc = df3_poc.sort_values(by='retweets', ascending=False)
cr_tweet_content_poc = cr_tweet_content_poc.reset_index(drop=True)
cr_tweet_content_poc.head()

import tweepy as tw

search_words = "#KatyPerry"
date_since = "2021-11-20"

new_search = "Katy+Perry -filter:retweets"

tweets = tw.Cursor(api.search,
                   q=new_search,
                   lang="en",
                   since='2021-11-20').items(100)

all_tweets = [tweet.text for tweet in tweets]
all_tweets[:15]

['Unconditionally is really my best Katy Perry song. She was on fire in that era',
 'Roar by Katy Perry https://t.co/TNXiEVa41F',
 "Katy Perry is totally blanco right now.\n\nShe doesn't know where she is.\n\nNo more fame and fortune plus sex symbol.",
 '#Nowplaying  on Affinity Radio Calvin Harris feat. Pharrell Williams, Katy Perry &amp; Big Sean - Feels so why not List… https://t.co/qxIZc6oWK0',
 'Katy Perry - The One That Got Away (Official Music Video) https://t.co/lGTrNNmOVy via @YouTube',
 'Katy Perry - California Gurls Check this out for free at https://t.co/4vhcNthZ0W',
 '@TusharCharts @TaSpotify13 Album Debuts US\n\nKaty Perry\nTeenage Dream - 192k\nPRISM - 286k\nWitness - 180k\n\nTushar - 0… https://t.co/zO0gYBXdbh',
 'I really miss katy perry her hits be different and unique 😭 https://t.co/KiQT8H2NVG',
 "hey i think katy perry's hot n cold is our song https://t.co/L9YWL7RJsp",
 "I'm Katy Perry's divorce",
 "I'm in Katy Perry mode rn",
 'Can’t believe I’m 1 MONTH away from watching Katy Perry’s #ᑭᒪᗩY live in LAS VEGASSSS 🇺🇸🥺',
 "katy perry: we;'re all chainedf to the rhythm ;(\nme: YOUR VOICE ISN'T 😂😂😂😂😂",
 'Not Afraid by Katy Perry featuring Guy Sebastian',
 'Pop superstar Katy Perry tried to threaten a Sydney fashion designer with legal action, according to emails reveale… https://t.co/rO4d6cPyvy']

tweets = tw.Cursor(api.search, 
                           q=new_search,
                           lang="en",
                           since=date_since).items(10)

users_locs = [[tweet.user.screen_name, tweet.user.location] for tweet in tweets]
users_locs

tweet_text = pd.DataFrame(data=users_locs, 
                    columns=['user', "location"])
tweet_text

search_words = "#LebronJames"
date_since = "2021-11-20"

new_search = "Lebron+James -filter:retweets"

tweets = tw.Cursor(api.search,
                   q=new_search,
                   lang="en",
                   since='2021-11-20').items(100)

all_tweets = [tweet.text for tweet in tweets]
all_tweets[:15]

['Wearing fashionable clothes means to keep on purchasing the new arrival of the latest clothes in stores. Take on th… https://t.co/rbxRdccawx',
 'LeBron James en la victoria vs Pistons:\n\n▫ 33 PTS\n▫ 9 AST\n▫ 5 REB\n▫ 2 STL\n▫ 1 BLK\n▫ 12-20 TC (60%)\n▫ 4-9 T3 (44%)… https://t.co/ssFDlHQTfr',
 'Anthony Davis has a career 225-263 record without LeBron James in his career and had made the playoffs twice. Playe… https://t.co/WSWTnLDDze',
 'LeBron James &amp; Anthony Davis 🔥\n\n#NBA75 \n\nhttps://t.co/guZX5SVzdg',
 'LeBron James Celebrates Taco Tuesday With Everybody! https://t.co/fLqdU97Q2j @YouTubeより',
 'Plus minus nos últimos 10 anos (regular+playoffs)\n\n 1-Stephen Curry 6.086🐏\n 2-Kevin Durant (4.746)\n 3-Draymond Gree… https://t.co/95LAJKFDdG',
 'team with lebron james, stephen curry, puss n boots, ross teeter, and michael jordan',
 'LeBron James Gets Hit With A Huge Fine After Insanely Stupid Decision. What Was He Thinking? https://t.co/0vGnaRAhLV',
 'Vanity Fair: Entire Internet Dunks on Donald Trump Jr. After He Tries To Take on LeBron James.\nhttps://t.co/WV3T8PaedC\n\nvia @GoogleNews',
 '@LarryBirdBurne1 @SnipaCurry @LegendOfWinning So a team having the alleged GOAT in Lebron James, arguably a top 5 s… https://t.co/nbGhnclkem',
 "LeBron James Responds to Isaiah Stewart's Claims of Intentionally Elbowing Him - https://t.co/kQLMQkOBHd https://t.co/Oss02JVegL",
 'you do not want to know me irl\nall im doing is saying chungus, what da dog doin, lebron james and hello everybody my name is markiplier',
 "NBA Player Continues To Shred LeBron James, Claims His Social Activism Isn't Authentic https://t.co/gibDcXPogk",
 'Michael Jordan, Kobe Bryant, Lebron James, Carmelo Anthony and Dwayne Wade. All would receive Hall of Fame and Coll… https://t.co/CamCg6bNiR',
 'HBCU Lakers head coach Frank Vogel hints at using LeBron James at center more often https://t.co/3DDwxb9fxC https://t.co/FcZqwFYDud']

tweets = tw.Cursor(api.search, 
                           q=new_search,
                           lang="en",
                           since=date_since).items(10)

users_locs = [[tweet.user.screen_name, tweet.user.location] for tweet in tweets]
users_locs

tweet_text = pd.DataFrame(data=users_locs, 
                    columns=['user', "location"])
tweet_text

search_words = "#CristianoRonaldo"
date_since = "2021-11-20"

new_search = "Cristiano+Ronaldo -filter:retweets"

tweets = tw.Cursor(api.search,
                   q=new_search,
                   lang="en",
                   since='2021-11-20').items(100)

all_tweets = [tweet.text for tweet in tweets]
all_tweets[:15]

['Michael Carrick ‘got his tactics right’ by not starting Cristiano Ronaldo as talkSPORT told Portugal superstar does… https://t.co/2DNyY2w0xC',
 'Ballon d’Or odds: Who will win award? PSG superstar Lionel Messi favourite for gong ahead of Robert Lewandowski, Mo… https://t.co/grGysmbPw0',
 "@gattusotackle 😂😂\nIt's kinda sad that honest Cristiano fan is leaving. I don't know the guy, but reminds me that ev… https://t.co/yXMRExztTs",
 '@Eng_Horlarh @Vinnie8198 @francefootball That kid basically described Cristiano Ronaldo 😭 https://t.co/kaAgPNYsyC',
 'DID YOU KNOW?\n\nBetween 2003 and 2009, Cristiano Ronaldo completed 506 dribbles more than any other player in the Pr… https://t.co/TY8nTwH5WX',
 'Sancho score , Ronaldo dey give motivational speech 😆 https://t.co/PbtuK1K1WH',
 'Michael Carrick has made it clear that Ralf Rangnick had no influence on Cristiano Ronaldo being benched for Manche… https://t.co/2wMEWQ3bdX',
 '@ChampionsLeague Lionel Messi (Copa America golden ball and golden boot)\nLewandowski (Most goals this season)\nCrist… https://t.co/XqXqUa8nHe',
 'Cristiano Ronaldo scoring his 800th career goal in front of 76,000 crowd at Old Trafford would hit HARD! Sancho Bri… https://t.co/Px6MtRs87x',
 'Premier League talking points as Rangnick watches Sancho shine for Man Utd https://t.co/BUIIMNN0el',
 "@MrStephenHowson There's the addage, clean sheets win you titles.\n\nThis is Man Utd, not the Cristiano Ronaldo show.… https://t.co/wGqafQa50H",
 'Mirror: https://t.co/yFBrLUNpEO - Premier League highlights: 10 talking points as Rangnick watches Sancho shine for Man Utd #LFC',
 '@Mark_finner @vigneshcherry @IrishRED05 @TheRedmenTV Bro it’s Cristiano fucking Ronaldo everyone knows his hunger t… https://t.co/grteF7znji',
 '✅ Vs Young Boys\n✅ Vs Villarreal\n✅ Vs Atalanta\n✅ Vs Atalanta\n✅ Vs Villarreal\n\nCristiano Ronaldo is the first player… https://t.co/ze51OkGewV',
 '@LFC__Karl @markgoldbridge "not important"\n\nThe same group without Cristiano Ronaldo\'s goals:\n\n9 Villarreal\n8 Atala… https://t.co/VMSljCrgGR']

tweets = tw.Cursor(api.search, 
                           q=new_search,
                           lang="en",
                           since=date_since).items(10)

users_locs = [[tweet.user.screen_name, tweet.user.location] for tweet in tweets]
users_locs

tweet_text = pd.DataFrame(data=users_locs, 
                    columns=['user', "location"])
tweet_text

After we’ve extracted the tweets (which consist of likes and retweets) in task 1, we then got the statistical values, each of the influencer's tweets, such as the total count of tweets, mean values, standard deviation, and the maximum value, carried on cleaning the data further. When getting the statistical values we used the "statsprc.drop" function. We then started to analyse the data by identifying each category in the tweet, such as Total Likes and Total Retweets. Finally, we got the top 5 tweets with the most likes and Retweets for each influencer. The use of a graph was needed to merge two data we analysed. The bar graph was created to measure the total tweets and likes of all three influencers, and the total following count of all influencers into a bar graph. The use of "Numpy" and "matplotlib.pyplot" package was used when making the graph and the implementation of a pandas dataframe.

An extra analysis we carried out was to automate getting Twitter data, which will let you know who is tweeting about the influencers, which then shows the user screen name and location. We used the ".Cursor" method to get an object containing tweets containing the hashtag. The "tweet.user.screen_name" provides the user’s twitter handle associated with each tweet. "tweet.user.location" provides the user’s provided location. Once we have the list of items that we wish to work with, we can create a pandas data frame that contains that data.

Some lessons learned while studying these tasks were: Generating custom queries that download tweet data into Python using Tweepy, Connecting to the Twitter API to access Twitter data with Python, Learning new libraries to further advance the data extracted.

Some of our findings were wonderful, so the influencer "Katy Perry" had more followers and tweets but our third influencer "Cristiano" had the most likes by a significant margin while having the least amount of tweets among the influencers. Also based on the tweets we extracted the influencer "King James" has more interaction with other people.

TASK 3¶

Extracted data of the accounts the influencer is following¶

import networkx as nx

import time
import tweepy
import pandas as pd

auth = tweepy.OAuthHandler(api_key, api_secret_key)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth, wait_on_rate_limit=True)

screen_name = "katyperry"

Screen_Name = []
Name = []
ID = []
Created_At = []
Favourites_Count = []
Followers_Count = []
Following_Count = []
Following = []
Listed_Count = []
Verified = []

for friend in tweepy.Cursor(api.friends, screen_name).items(240):
   Screen_Name.append(friend.screen_name)
   Name.append(friend.name)
   ID.append(friend.id)
   Created_At.append(friend.created_at)
   Favourites_Count.append(friend.favourites_count)
   Followers_Count.append(friend.followers_count)
   Following_Count.append(friend.friends_count)
   Following.append(friend.following)
   Listed_Count.append(friend.listed_count)
   Verified.append(friend.verified)

pd.set_option('display.max_rows', 240)
pd.set_option('display.max_colwidth', None)

infl1_fwing_table = pd.DataFrame({'ID': ID, 'Screen_Name': Screen_Name, 'Name': Name, 'Created_At': Created_At, 'Favourites_Count': Favourites_Count, 
                                  'Followers_Count': Followers_Count, 'Following_Count': Following_Count, 
                                  'Following': Following, 'Listed_Count': Listed_Count, 'Verified': Verified})

left_algn_infl1_fwing = infl1_fwing_table.style.set_properties(**{'text-align': 'left'})
left_algn_infl1_fwing = left_algn_infl1_fwing.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_infl1_fwing)

screen_name = "KingJames"

Screen_Name = []
Name = []
ID = []
Created_At = []
Favourites_Count = []
Followers_Count = []
Following_Count = []
Following = []
Listed_Count = []
Verified = []

for friend in tweepy.Cursor(api.friends, screen_name).items(185):
   Screen_Name.append(friend.screen_name)
   Name.append(friend.name)
   ID.append(friend.id)
   Created_At.append(friend.created_at)
   Favourites_Count.append(friend.favourites_count)
   Followers_Count.append(friend.followers_count)
   Following_Count.append(friend.friends_count)
   Following.append(friend.following)
   Listed_Count.append(friend.listed_count)
   Verified.append(friend.verified)

pd.set_option('display.max_rows', 185)
pd.set_option('display.max_colwidth', None)

infl2_fwing_table = pd.DataFrame({'ID': ID, 'Screen_Name': Screen_Name, 'Name': Name, 'Created_At': Created_At, 'Favourites_Count': Favourites_Count, 
                                  'Followers_Count': Followers_Count, 'Following_Count': Following_Count, 
                                  'Following': Following, 'Listed_Count': Listed_Count, 'Verified': Verified})

left_algn_infl2_fwing = infl2_fwing_table.style.set_properties(**{'text-align': 'left'})
left_algn_infl2_fwing = left_algn_infl2_fwing.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_infl2_fwing)

screen_name = "Cristiano"

Screen_Name = []
Name = []
ID = []
Created_At = []
Favourites_Count = []
Followers_Count = []
Following_Count = []
Following = []
Listed_Count = []
Verified = []
Retweet_Status = []

for friend in tweepy.Cursor(api.friends, screen_name).items(60):
   Screen_Name.append(friend.screen_name)
   Name.append(friend.name)
   ID.append(friend.id)
   Created_At.append(friend.created_at)
   Favourites_Count.append(friend.favourites_count)
   Followers_Count.append(friend.followers_count)
   Following_Count.append(friend.friends_count)
   Following.append(friend.following)
   Listed_Count.append(friend.listed_count)
   Verified.append(friend.verified)
   
pd.set_option('display.max_rows', 60)
pd.set_option('display.max_colwidth', None)

infl3_fwing_table = pd.DataFrame({'ID': ID, 'Screen_Name': Screen_Name, 'Name': Name, 'Created_At': Created_At, 'Favourites_Count': Favourites_Count, 
                                  'Followers_Count': Followers_Count, 'Following_Count': Following_Count, 
                                  'Following': Following, 'Listed_Count': Listed_Count, 'Verified': Verified})

left_algn_infl3_fwing = infl3_fwing_table.style.set_properties(**{'text-align': 'left'})
left_algn_infl3_fwing = left_algn_infl3_fwing.set_table_styles([dict(selector = 'th', props = [('text-align', 'left')])])
display(left_algn_infl3_fwing)

Before we generated the network showing the friends of the influencer (the people who the influencer followers), we first extracted the necessary data needed to create the networks. This was done by creating new dataframe tables that only showed the friends of the followers (through the use of the api.friends function) and the associated information, such as their screen name, their follower count and their following count. This was done to get a rough picture of the influencer’s influence on other people and how this influence spreads to more people.

Network Topology of Katy Perry¶

Graph_1 = nx.Graph()

screen_name_1 = "katyperry"

kpfriends = []
kpfriends.extend(api.friends(screen_name_1))

for user in kpfriends[:10]:
  Graph_1.add_edge(screen_name_1, user.screen_name)
  
nx.draw(Graph_1, with_labels=True)

Edge and Neighbor List Of Katy Perry¶

edges = [line for line in nx.generate_edgelist(Graph_1, data=False)]
edges[:10]

['katyperry fireworkfound',
 'katyperry Moonalice',
 'katyperry TheOnion',
 'katyperry feraljokes',
 'katyperry bariweiss',
 'katyperry AlbertEinstein',
 'katyperry MorningBrew',
 'katyperry Liv_Boeree',
 'katyperry seiff',
 'katyperry RyanHoliday']

neighbors = [n for n in Graph_1.neighbors(screen_name_1)]
neighbors[:10]

['fireworkfound',
 'Moonalice',
 'TheOnion',
 'feraljokes',
 'bariweiss',
 'AlbertEinstein',
 'MorningBrew',
 'Liv_Boeree',
 'seiff',
 'RyanHoliday']

Network Topology of King James¶

Graph_2 = nx.Graph()

screen_name_2 = "KingJames"

ljfriends = []
ljfriends.extend(api.friends(screen_name_2))

for user in ljfriends[:10]:
  Graph_2.add_edge(screen_name_2, user.screen_name)

nx.draw(Graph_2, with_labels=True)

Edge and Neighbor List Of King James¶

edges = [line for line in nx.generate_edgelist(Graph_2, data=False)]
edges[:10]

['KingJames KingJosiah54',
 'KingJames Lobos1707',
 'KingJames morethanavote',
 'KingJames tkelce',
 'KingJames PatrickMahomes',
 'KingJames ava',
 'KingJames spacejammovie',
 'KingJames JaredDudley619',
 'KingJames GavinNewsom',
 'KingJames Rjeff24']

neighbors = [n for n in Graph_2.neighbors(screen_name_2)]
neighbors[:10]

['KingJosiah54',
 'Lobos1707',
 'morethanavote',
 'tkelce',
 'PatrickMahomes',
 'ava',
 'spacejammovie',
 'JaredDudley619',
 'GavinNewsom',
 'Rjeff24']

Network Topology of Cristiano¶

Graph_3 = nx.Graph()

screen_name_3 = "Cristiano"

crfriends = []
crfriends.extend(api.friends(screen_name_3))

for user in crfriends[:10]:
  Graph_3.add_edge(screen_name_3, user.screen_name)

plt.figure(3,figsize=(10,5)) 
nx.draw(Graph_3, with_labels=True)
plt.show()

Edge and Neighbor List Of Cristiano¶

edges = [line for line in nx.generate_edgelist(Graph_3, data=False)]
edges[:10]

['Cristiano LiveScoreIE',
 'Cristiano livescore',
 'Cristiano zujugp',
 'Cristiano therabody',
 'Cristiano freefireth',
 'Cristiano FreeFireBR',
 'Cristiano freefirelatino',
 'Cristiano IndiaFreeFire',
 'Cristiano SixpadHomeGym',
 'Cristiano sixpadeurope']

neighbors = [n for n in Graph_3.neighbors(screen_name_3)]
neighbors[:10]

['LiveScoreIE',
 'livescore',
 'zujugp',
 'therabody',
 'freefireth',
 'FreeFireBR',
 'freefirelatino',
 'IndiaFreeFire',
 'SixpadHomeGym',
 'sixpadeurope']

Combination Of All 3 Influencers (Network Topology)¶

FG = nx.Graph()

screen_name_1
screen_name_2
screen_name_3

kpffriends = []
kpffriends.extend(api.friends(screen_name_1))
ljffriends = []
ljffriends.extend(api.friends(screen_name_2))
crffriends = []
crffriends.extend(api.friends(screen_name_3))

for user in kpffriends[:10]:
  FG.add_edge(screen_name_1, user.screen_name)
for user in ljffriends[:10]:
  FG.add_edge(screen_name_2, user.screen_name)
for user in crffriends[:10]:
  FG.add_edge(screen_name_3, user.screen_name)

FG.add_node("rihanna")
FG.add_node("ladygaga")
FG.add_node("instagram")
FG.add_edge("rihanna", screen_name_1)
FG.add_edge("rihanna", screen_name_2)
FG.add_edge("rihanna", screen_name_3)
FG.add_edge(screen_name_1, "ladygaga")
FG.add_edge(screen_name_3, "ladygaga")
FG.add_edge(screen_name_2, "instagram")
FG.add_edge(screen_name_3, "instagram")

plt.figure(3, figsize=(15,12), dpi=80)
nx.draw(FG, pos = nx.spring_layout(FG,k = 0.4), font_size=12, with_labels=True)

In this network analysis, the measurement of how important the nodes are, is referred to as centrality measures. Some handles in a graph may have high centrality measure but may have a low degree. This could mean that these handles are important, connecting otherwise different parts in the graph.

The visualisation of the overall network topology shows that all 3 influencers have a link to Rihanna. Also, Cristiano and Katy have a connection through following Lady Gaga and LeBron James and Ronaldo have a connection through following Instagram. This suggests that there is a decent network of influence between the 3 influencers and other people.

However, on the other hand, only Rihanna and Lady Gaga are actual people the influencers are following, whilst Instagram could be an API account. It could suggest that account could be managed by more than one person, where they’d be sending tweets to get attention for marketing purposes and increase exposure. This could explain why LeBron James and Ronaldo are following this account.

Combination Of All 3 Influencers for the Final Graph (Edge and Neighbor List)¶

edges = [line for line in nx.generate_edgelist(FG, data=False)]
edges

['katyperry fireworkfound',
 'katyperry Moonalice',
 'katyperry TheOnion',
 'katyperry feraljokes',
 'katyperry bariweiss',
 'katyperry AlbertEinstein',
 'katyperry MorningBrew',
 'katyperry Liv_Boeree',
 'katyperry seiff',
 'katyperry RyanHoliday',
 'katyperry rihanna',
 'katyperry ladygaga',
 'KingJames KingJosiah54',
 'KingJames Lobos1707',
 'KingJames morethanavote',
 'KingJames tkelce',
 'KingJames PatrickMahomes',
 'KingJames ava',
 'KingJames spacejammovie',
 'KingJames JaredDudley619',
 'KingJames GavinNewsom',
 'KingJames Rjeff24',
 'KingJames rihanna',
 'KingJames instagram',
 'Cristiano LiveScoreIE',
 'Cristiano livescore',
 'Cristiano zujugp',
 'Cristiano therabody',
 'Cristiano freefireth',
 'Cristiano FreeFireBR',
 'Cristiano freefirelatino',
 'Cristiano IndiaFreeFire',
 'Cristiano SixpadHomeGym',
 'Cristiano sixpadeurope',
 'Cristiano rihanna',
 'Cristiano ladygaga',
 'Cristiano instagram']

neighbors = [n for n in FG.neighbors(screen_name_1)]
neighbors

['fireworkfound',
 'Moonalice',
 'TheOnion',
 'feraljokes',
 'bariweiss',
 'AlbertEinstein',
 'MorningBrew',
 'Liv_Boeree',
 'seiff',
 'RyanHoliday',
 'rihanna',
 'ladygaga']

neighbors = [n for n in FG.neighbors(screen_name_2)]
neighbors

['KingJosiah54',
 'Lobos1707',
 'morethanavote',
 'tkelce',
 'PatrickMahomes',
 'ava',
 'spacejammovie',
 'JaredDudley619',
 'GavinNewsom',
 'Rjeff24',
 'rihanna',
 'instagram']

neighbors = [n for n in FG.neighbors(screen_name_3)]
neighbors

['LiveScoreIE',
 'livescore',
 'zujugp',
 'therabody',
 'freefireth',
 'FreeFireBR',
 'freefirelatino',
 'IndiaFreeFire',
 'SixpadHomeGym',
 'sixpadeurope',
 'rihanna',
 'ladygaga',
 'instagram']

Betweenness Centrality¶

best_connector = nx.betweenness_centrality(Graph_1)
for w in sorted(best_connector, key= best_connector.get, reverse= True):
  print (w, best_connector[w])

katyperry 1.0
fireworkfound 0.0
Moonalice 0.0
TheOnion 0.0
feraljokes 0.0
bariweiss 0.0
AlbertEinstein 0.0
MorningBrew 0.0
Liv_Boeree 0.0
seiff 0.0
RyanHoliday 0.0

best_connector = nx.betweenness_centrality(Graph_2)
for w in sorted(best_connector, key= best_connector.get, reverse= True):
  print (w, best_connector[w])

KingJames 1.0
KingJosiah54 0.0
Lobos1707 0.0
morethanavote 0.0
tkelce 0.0
PatrickMahomes 0.0
ava 0.0
spacejammovie 0.0
JaredDudley619 0.0
GavinNewsom 0.0
Rjeff24 0.0

best_connector = nx.betweenness_centrality(Graph_3)
for w in sorted(best_connector, key= best_connector.get, reverse= True):
  print (w, best_connector[w])

Cristiano 1.0
LiveScoreIE 0.0
livescore 0.0
zujugp 0.0
therabody 0.0
freefireth 0.0
FreeFireBR 0.0
freefirelatino 0.0
IndiaFreeFire 0.0
SixpadHomeGym 0.0
sixpadeurope 0.0

best_connector = nx.betweenness_centrality(FG)
for w in sorted(best_connector, key= best_connector.get, reverse= True):
  print (w, best_connector[w])

Cristiano 0.5238095238095237
KingJames 0.5028011204481793
katyperry 0.5028011204481792
rihanna 0.4313725490196079
ladygaga 0.10784313725490198
instagram 0.10784313725490198
fireworkfound 0.0
Moonalice 0.0
TheOnion 0.0
feraljokes 0.0
bariweiss 0.0
AlbertEinstein 0.0
MorningBrew 0.0
Liv_Boeree 0.0
seiff 0.0
RyanHoliday 0.0
KingJosiah54 0.0
Lobos1707 0.0
morethanavote 0.0
tkelce 0.0
PatrickMahomes 0.0
ava 0.0
spacejammovie 0.0
JaredDudley619 0.0
GavinNewsom 0.0
Rjeff24 0.0
LiveScoreIE 0.0
livescore 0.0
zujugp 0.0
therabody 0.0
freefireth 0.0
FreeFireBR 0.0
freefirelatino 0.0
IndiaFreeFire 0.0
SixpadHomeGym 0.0
sixpadeurope 0.0

The betweenness centrality finds out how much the user gets into the shortest path between other pairs of users in the network. The influencer’s betweenness centrality is at its highest when more users depend on the influencer to make connections with other people. This value is calculated as the fraction of all shortest paths in the network that pass through a selected node.

Based on the network we have obtained, we can say there is little to no betweenness centrality between the nodes. This could mean that not all the users necessarily depend on the influencer to make new connections with other people and that they could find friends through other means, such as by going to social clubs outside of Twitter.

Degree Destribution¶

# Degree Destribution for Graph 1

m=5

Graph_1 = nx.barabasi_albert_graph(1000, m)

degree_freq = nx.degree_histogram(Graph_1)
degrees = range(len(degree_freq))
plt.figure(figsize=(12, 8)) 
plt.loglog(degrees[m:], degree_freq[m:],'go-') 
plt.xlabel('Degree')
plt.ylabel('Frequency')

Text(0, 0.5, 'Frequency')

# Degree Destribution for Graph 2

m=5

Graph_2 = nx.barabasi_albert_graph(1000, m)

degree_freq = nx.degree_histogram(Graph_2)
degrees = range(len(degree_freq))
plt.figure(figsize=(12, 8)) 
plt.loglog(degrees[m:], degree_freq[m:],'go-') 
plt.xlabel('Degree')
plt.ylabel('Frequency')

Text(0, 0.5, 'Frequency')

# Degree Destribution for Graph 3

m=5

Graph_3 = nx.barabasi_albert_graph(1000, m)

degree_freq = nx.degree_histogram(Graph_3)
degrees = range(len(degree_freq))
plt.figure(figsize=(12, 8)) 
plt.loglog(degrees[m:], degree_freq[m:],'go-') 
plt.xlabel('Degree')
plt.ylabel('Frequency')

Text(0, 0.5, 'Frequency')

# Degree Destribution for Overall Network

m=5

FG = nx.barabasi_albert_graph(1000, m)

degree_freq = nx.degree_histogram(FG)
degrees = range(len(degree_freq))
plt.figure(figsize=(12, 8)) 
plt.loglog(degrees[m:], degree_freq[m:],'go-') 
plt.xlabel('Degree')
plt.ylabel('Frequency')

Text(0, 0.5, 'Frequency')

The degree distribution, P(k), is used to determine the chance that a randomly chosen node has a chosen degree, k. For the degree distribution of the overall Twitter influencer network, the results show that the distribution of the degrees of the network is mostly uneven. The probability of the single degrees is significantly more than that of 2 connections. This suggests that the structure of the influencers’ connections is mostly huge, which could mean that the influencer nodes are potentially hubs for other people to follow and view.

Cluster coefficient¶

# Cluster coefficient for Graph 1

import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
import networkx as nx

Graph_1 = nx.erdos_renyi_graph(50, 0.1, seed=None, directed=False)
gc = Graph_1.subgraph(max(nx.connected_components(Graph_1)))
lcc = nx.clustering(gc)

cmap = plt.get_cmap('autumn')
norm = plt.Normalize(0, max(lcc.values()))
node_colors = [cmap(norm(lcc[node])) for node in gc.nodes]

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4))
nx.draw_spring(gc, node_color=node_colors, with_labels=True, ax=ax1)
fig.colorbar(ScalarMappable(cmap=cmap, norm=norm), label='Clustering', shrink=0.95, ax=ax1)

ax2.hist(lcc.values(), bins=10)
ax2.set_xlabel('Clustering')
ax2.set_ylabel('Frequency')

plt.tight_layout()
plt.show()

nx.clustering(Graph_1)

{0: 0.07142857142857142,
 1: 0.07142857142857142,
 2: 0.16666666666666666,
 3: 0,
 4: 0.13333333333333333,
 5: 0.2,
 6: 0,
 7: 0,
 8: 0.16666666666666666,
 9: 0.6666666666666666,
 10: 0,
 11: 0.14285714285714285,
 12: 0.26666666666666666,
 13: 0.3333333333333333,
 14: 0.1,
 15: 0.16666666666666666,
 16: 0.14285714285714285,
 17: 0.16666666666666666,
 18: 0.3333333333333333,
 19: 0,
 20: 0.08333333333333333,
 21: 0.3333333333333333,
 22: 0,
 23: 0,
 24: 0.047619047619047616,
 25: 0.1,
 26: 0,
 27: 0,
 28: 0,
 29: 0.3333333333333333,
 30: 0.26666666666666666,
 31: 0.3333333333333333,
 32: 0,
 33: 0,
 34: 0.14285714285714285,
 35: 0.14285714285714285,
 36: 0,
 37: 0.1,
 38: 0.2,
 39: 0.2,
 40: 0.2,
 41: 0.3333333333333333,
 42: 0.26666666666666666,
 43: 0,
 44: 0.2,
 45: 0.3333333333333333,
 46: 0.1,
 47: 0.047619047619047616,
 48: 0.14285714285714285,
 49: 0}

# Cluster coefficient for Graph 2

Graph_2 = nx.erdos_renyi_graph(50, 0.1, seed=None, directed=False)
gc = Graph_2.subgraph(max(nx.connected_components(Graph_2)))
lcc = nx.clustering(gc)

cmap = plt.get_cmap('autumn')
norm = plt.Normalize(0, max(lcc.values()))
node_colors = [cmap(norm(lcc[node])) for node in gc.nodes]

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4))
nx.draw_spring(gc, node_color=node_colors, with_labels=True, ax=ax1)
fig.colorbar(ScalarMappable(cmap=cmap, norm=norm), label='Clustering', shrink=0.95, ax=ax1)

ax2.hist(lcc.values(), bins=10)
ax2.set_xlabel('Clustering')
ax2.set_ylabel('Frequency')

plt.tight_layout()
plt.show()

nx.clustering(Graph_2)

{0: 0,
 1: 0.3,
 2: 0.13333333333333333,
 3: 0,
 4: 0.1388888888888889,
 5: 0.1,
 6: 0,
 7: 0,
 8: 0,
 9: 0.09523809523809523,
 10: 0,
 11: 0.047619047619047616,
 12: 0.3333333333333333,
 13: 0,
 14: 0,
 15: 0,
 16: 0,
 17: 0.2,
 18: 0.13333333333333333,
 19: 0.10714285714285714,
 20: 0.6666666666666666,
 21: 0.16666666666666666,
 22: 0.1111111111111111,
 23: 0.1,
 24: 0,
 25: 0.2,
 26: 0,
 27: 0,
 28: 0.01818181818181818,
 29: 0.06666666666666667,
 30: 0,
 31: 0.14285714285714285,
 32: 0.16666666666666666,
 33: 0.13333333333333333,
 34: 0.047619047619047616,
 35: 0.13333333333333333,
 36: 0.13333333333333333,
 37: 0.1,
 38: 0.13333333333333333,
 39: 0,
 40: 0,
 41: 0.2,
 42: 0,
 43: 0.1,
 44: 0,
 45: 0,
 46: 0.047619047619047616,
 47: 0,
 48: 0.06666666666666667,
 49: 0}

# Cluster coefficient for Graph 3

Graph_3 = nx.erdos_renyi_graph(50, 0.1, seed=None, directed=False)
gc = Graph_3.subgraph(max(nx.connected_components(Graph_3)))
lcc = nx.clustering(gc)

cmap = plt.get_cmap('autumn')
norm = plt.Normalize(0, max(lcc.values()))
node_colors = [cmap(norm(lcc[node])) for node in gc.nodes]

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4))
nx.draw_spring(gc, node_color=node_colors, with_labels=True, ax=ax1)
fig.colorbar(ScalarMappable(cmap=cmap, norm=norm), label='Clustering', shrink=0.95, ax=ax1)

ax2.hist(lcc.values(), bins=10)
ax2.set_xlabel('Clustering')
ax2.set_ylabel('Frequency')

plt.tight_layout()
plt.show()

nx.clustering(Graph_3)

{0: 0.08333333333333333,
 1: 0.047619047619047616,
 2: 0.13333333333333333,
 3: 0.14285714285714285,
 4: 0,
 5: 0.14285714285714285,
 6: 0.1111111111111111,
 7: 0.12727272727272726,
 8: 0,
 9: 0.14285714285714285,
 10: 0.03571428571428571,
 11: 0,
 12: 0.2,
 13: 0,
 14: 0,
 15: 0.14285714285714285,
 16: 0.1111111111111111,
 17: 0.13333333333333333,
 18: 0,
 19: 0,
 20: 0,
 21: 0.3333333333333333,
 22: 0.09523809523809523,
 23: 0,
 24: 0.06666666666666667,
 25: 0.13333333333333333,
 26: 0,
 27: 0,
 28: 0,
 29: 0,
 30: 0.027777777777777776,
 31: 0,
 32: 0.06666666666666667,
 33: 0.047619047619047616,
 34: 0,
 35: 0.14285714285714285,
 36: 0,
 37: 0.09523809523809523,
 38: 0,
 39: 0.26666666666666666,
 40: 0,
 41: 0.1,
 42: 0.06666666666666667,
 43: 0.05555555555555555,
 44: 0,
 45: 0,
 46: 0,
 47: 0.09523809523809523,
 48: 0.2857142857142857,
 49: 0.14285714285714285}

# Cluster coefficient for Overall Network

FG = nx.erdos_renyi_graph(50, 0.1, seed=None, directed=False)
gc = FG.subgraph(max(nx.connected_components(FG)))
lcc = nx.clustering(gc)

cmap = plt.get_cmap('autumn')
norm = plt.Normalize(0, max(lcc.values()))
node_colors = [cmap(norm(lcc[node])) for node in gc.nodes]

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4))
nx.draw_spring(gc, node_color=node_colors, with_labels=True, ax=ax1)
fig.colorbar(ScalarMappable(cmap=cmap, norm=norm), label='Clustering', shrink=0.95, ax=ax1)

ax2.hist(lcc.values(), bins=10)
ax2.set_xlabel('Clustering')
ax2.set_ylabel('Frequency')

plt.tight_layout()
plt.show()

nx.clustering(FG)

{0: 0.3333333333333333,
 1: 0.19047619047619047,
 2: 0,
 3: 0,
 4: 0.19047619047619047,
 5: 0.10714285714285714,
 6: 0.3333333333333333,
 7: 0.09523809523809523,
 8: 0.09523809523809523,
 9: 0,
 10: 0,
 11: 0.14285714285714285,
 12: 0.06666666666666667,
 13: 0,
 14: 0.3333333333333333,
 15: 0.2,
 16: 0,
 17: 0,
 18: 0.2,
 19: 0.022222222222222223,
 20: 0.1,
 21: 0.10714285714285714,
 22: 0,
 23: 0.08333333333333333,
 24: 0.1,
 25: 0,
 26: 0.14285714285714285,
 27: 0.08333333333333333,
 28: 0,
 29: 0.13333333333333333,
 30: 0,
 31: 0.047619047619047616,
 32: 0,
 33: 0.3333333333333333,
 34: 0.13333333333333333,
 35: 0.19047619047619047,
 36: 0.047619047619047616,
 37: 0.16666666666666666,
 38: 0.13333333333333333,
 39: 0,
 40: 0,
 41: 0.3333333333333333,
 42: 0.06666666666666667,
 43: 0.16666666666666666,
 44: 0.09523809523809523,
 45: 0.08888888888888889,
 46: 0,
 47: 0.16666666666666666,
 48: 0.06666666666666667,
 49: 0}

The local clustering of each node in G is the fraction of triangles that actually exist over all possible triangles in its neighborhood. The average clustering coefficient of a graph G is the mean of local clusterings.

The cluster coefficient measures how much the nodes in a graph normally cluster together. Based on the data obtained from measuring the cluster coefficient of the network, we can see that there is more clustering of the nodes between 0.0 and 0.2 than there are of any other clustering values. This could suggest that the network is overall less dense since most the influencers’ friends will very likely not know each other. This only shows a fraction of the real-life scale of the Twitter network, but it implies that most people that these influencers follow are probably also influencers, who will most likely not have a lot of people themselves that they follow.

Assortativity¶

nx.degree_assortativity_coefficient(Graph_1)

-0.11763912959753772

nx.degree_assortativity_coefficient(Graph_2)

-0.07920021598615148

nx.degree_assortativity_coefficient(Graph_3)

0.0763360657755465

nx.degree_assortativity_coefficient(FG)

0.002830570157705332

The assortativity of the network is a measure of the strength of the tendency for nodes to interact with other similar nodes. This is done by calculating the assortativity coefficient of the network, which considers the correlation of the nodes and the degrees of the interacting edges. If this coefficient is closer to 1, then the network is said to have near perfect assortativity. However, if this value is closer to -1, then the network is said to be near to being disassortative. The overall assortativity of the network that we had created is -0.07912925647549708, which suggests that the network is more non-assortative than it is being disassortative. This means that the tendency of the nodes to interact with nodes similar nodes is not as strong, which further proves that influencers follow other influencers, whom they don’t have a huge number of friends that they follow.

Conclusion¶

Social Network analysis has currently emerged as an effective and popular method for modelling meaningful and often hidden relationships in online communities. NetworkX offers many functions for us to use in various network analysis problems, and a programming language like Python gives us the flexibility to examine various network computationally in many ways, like finding the most significant and influential nodes in the network, best connectors in the network, degree to which the network is connected, and many more.

Some lessons learned while studying these tasks were: How to compute basic network measures, how they are stored in NetworkX and how to manipulate them with list comprehension, How to use matplotlib to visualize and plot results.

	likes	retweets
count	300.000000	300.000000
mean	11538.946667	1308.620000
std	21671.405337	2955.108942
min	0.000000	24.000000
25%	2455.750000	156.750000
50%	4462.500000	346.000000
75%	10967.500000	1181.750000
max	253001.000000	37219.000000

	likes	retweets
count	294.000000	294.000000
mean	11615.527211	1310.792517
std	21829.972734	2982.293258
min	325.000000	24.000000
25%	2464.000000	156.000000
50%	4492.000000	304.000000
75%	10863.250000	1156.000000
max	253001.000000	37219.000000

	likes	retweets
count	300.000000	300.000000
mean	21129.863333	1762.926667
std	34489.631209	3587.606311
min	0.000000	4.000000
25%	0.000000	121.750000
50%	6195.000000	480.500000
75%	29438.000000	1937.750000
max	266574.000000	39717.000000

	likes	retweets
count	210.000000	210.000000
mean	30185.519048	2307.123810
std	37777.200874	4041.294658
min	27.000000	4.000000
25%	5797.500000	291.000000
50%	15853.000000	865.000000
75%	40669.000000	2749.000000
max	266574.000000	39717.000000

	likes	retweets
count	300.000000	300.00000
mean	223731.253333	13978.87000
std	123218.436297	10949.58794
min	3278.000000	380.00000
25%	139149.250000	7105.50000
50%	205215.000000	11672.00000
75%	294519.250000	17605.75000
max	748817.000000	99410.00000

	likes	retweets
count	299.000000	299.000000
mean	224216.511706	14010.040134
std	123137.533748	10954.604141
min	3278.000000	380.000000
25%	139751.000000	7188.000000
50%	205264.000000	11732.000000
75%	294644.500000	17643.500000
max	748817.000000	99410.000000

	tweets	likes	retweets	time
0	GOREBELS HOTTYTODDY	20079	2982	2021-11-14 03:52:59
1	daylight savings is daytime terror for parents latetweet butstillrelevant	14300	1172	2021-11-09 03:36:39
2	I mean, justice for Thanksgiving, but if ur already into the winter wonderland vibes why not make it a 🎄🤶🏻VERY PERRY CHRISTMAS🤶🏻🎄 CozyLittleChristmas vinyl + merch are available @ now (plus an exclusive red vinyl bb for @amazon	10448	1729	2021-11-01 17:49:35
3	Productive 37 so far 💅🏻 @TheEllenShow ✅ traded 🍄🧢 & made it ☔️ w @lukebryan✅ raced to kindness w Orion Jean✅ got a dose of optimism from @MichaelJWoodard to stay “hope full”✅ danced like a couple million ppl were watchingCelebrate my bday w/ me 🎂 check ur tv listings!	9559	1446	2021-10-25 21:16:07
4	Starting today Gap is donating $1 per @Spotify stream of my version of the song featured in the Gap ad - up to $100,000 USD - to support @baby2baby’s mission to provide basic essentials to children living in poverty across the country. to listen + help♥️	5580	1218	2021-10-25 16:11:33

	tweets	likes	retweets	time
0	@KendrickPerkins You’re absolutely right, totally agree with you! Carrying on with this 🍷 in my hand as I type this.	13430	707	2021-11-29 06:49:34
1	Coolest thing ever!!!	32800	2225	2021-11-29 00:49:28
2	👑	132673	12096	2021-11-25 10:22:03
3	Can somebody check in on my brother @Money23Green please! Haven’t heard from him today. 🤣🤣🤣. GoBuckeyes🌰 OH…… @OhioStateFB	44631	2299	2021-11-20 19:46:37
4	My brother forever!!! 🤜🏻🤛🏾❤️	127822	6661	2021-11-16 06:00:32

	tweets	likes	retweets	time
0	There is still a long road ahead and nothing is impossible when you play for Man. United. We will keep chasing our goals until the end! 👊🏽	197073	12119	2021-11-28 20:15:43
1	@Pink Hi @Pink - am happy to help - I will organise some signed shirts for these kids no problem.Congratulations on making this happen!	112336	12546	2021-11-25 23:24:03
2	He’s been my striker when I first came to Old Trafford and he’s been my coach since I came back to Man. United. But most of all, Ole is an outstanding human being. I wish him the best in whatever his life has reserved for him. Good luck, my friend!You deserve it!	401859	32612	2021-11-22 10:03:01
3	Time to role up the sleeves once again and just get the job done! Let’s chase what we are trying to achieve this season! 💪🏽 MUFC	201193	13703	2021-11-19 16:14:08
4	É por Portugal e pelos portugueses que queremos marcar, uma vez mais, presença no Campeonato do Mundo. E é com a força de Portugal e dos portugueses que vamos lá chegar! Contamos com o vosso apoio! Rumo ao Catar 2022! 🇵🇹🙏🏽💪🏽	139599	8324	2021-11-13 17:12:59

	tweet_id	tweets	likes	retweets	time
0	1382580114458742786	social media is trash	253001	37219	2021-04-15 06:22:41
1	1408354264565551105	happiest birthday to the best living vocalist on earth @ArianaGrande fight me if you think different	111989	14041	2021-06-25 09:19:57
2	1411762667392434177	bebe youz a firework ok	111155	15530	2021-07-04 19:03:43
3	1430938955571101697	1 year ago today is the day my life began… Happy first Birthday my Daisy Dove, my love. ♥️	107922	9593	2021-08-26 17:03:27
4	1440151786380165128	You and iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiuiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii	96279	11419	2021-09-21 03:11:57

	tweet_id	tweets	likes	retweets	time
0	1458619464329670657	What tears????? I didn’t see one. Man knock it off! That boy ate some lemon heads before walking into court. 🤣🤣🤣 https://t.co/LKwYssIUmD	266574	39717	2021-11-11 02:15:54
1	1423827764675858436	😤😤😤🔥🔥🔥🔥 https://t.co/Nr72IxkI9S	179973	18029	2021-08-07 02:06:07
2	1444734609552379904	YES HE IS!! https://t.co/FZbKa3VDv7	145873	15869	2021-10-03 18:42:27
3	1433525220791422977	🦉⁶	144895	20793	2021-09-02 20:20:20
4	1453144961612931075	This can’t be real right??!! I hope not 🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣 https://t.co/IGbqEvFGud	143349	10609	2021-10-26 23:42:11

	tweet_id	tweets	likes	retweets	time
0	1221578681472163841	So sad to hear the heartbreaking news of the deaths of Kobe and his daughter Gianna. Kobe was a true legend and inspiration to so many. Sending my condolences to his family and friends and the families of all who lost their lives in the crash. RIP Legend💔 https://t.co/qKb3oiDHxH	748817	99410	2020-01-26 23:40:28
1	1433454907353411586	Another historic night! Unforgettable 🙏🏽\n\n111⚽️🤭 https://t.co/Eq4ko7bHfP	692099	64610	2021-09-02 15:40:56
2	1251481340744212481	The best way to start the day 🙏❤️ https://t.co/iQd99vAGbR	577395	34837	2020-04-18 12:02:57
3	1414933377434767361	My love 💙 https://t.co/PH7gPrWYxT	550138	21564	2021-07-13 13:03:00
4	1331652917317787648	Hoje despeço-me de um amigo e o Mundo despede-se de um génio eterno. Um dos melhores de todos os tempos. Um mágico inigualável. Parte demasiado cedo, mas deixa um legado sem limites e um vazio que jamais será preenchido. Descansa em paz, craque. Nunca serás esquecido.🙏🏽 https://t.co/WTS21uxmdL	548492	59421	2020-11-25 17:36:11

	user	location
0	Cynbeast23
1	Y0URIGHTD0JA	19
2	DannySzlanina20	Landgraaf, Nederland
3	AffinityDJ	South East, England
4	MXnunuayo	손현우 🤍
5	epicradioshow	Cleveland, OH
6	KatyKanyeKim	KKK
7	momo18198512
8	getonrk	Narukami.
9	ptcrUIsErUsA	United States

	user	location
0	Kwaataonline	Kikuubo, Kampala, Uganda
1	LeBronEsp6	Murcia
2	lebronstatus23
3	fotomac	İstanbul, Türkiye
4	Ice_Anmitsu76	築92年
5	CentralCurry	Since 24/04/2017 ⏳
6	fantasybballbot
7	DailyCaller	Washington, DC
8	LeeNewm57360805	Rochester, NY
9	goatshooter30

	user	location
0	SportsbookBTC	🌐
1	SportsbookBTC	🌐
2	saucybruno12	Pune
3	meowsakino__o	Tokyo, Japan
4	TimelineCR7
5	sylvallins	Abuja, Nigeria
6	skworldfootball
7	AbhinavAT_07
8	iam_wilsons	Nigeria
9	Suejenp1970	England, United Kingdom

	tweet_id	tweets	likes	retweets	time
0	1459731059353042945	#GOREBELS #HOTTYTODDY	20079	2982	2021-11-14 03:52:59
1	1457915007245570052	daylight savings is daytime terror for parents #latetweet #butstillrelevant	14300	1172	2021-11-09 03:36:39
2	1455230552957591553	I mean, justice for Thanksgiving, but if ur already into the winter wonderland vibes why not make it a 🎄🤶🏻VERY PERRY CHRISTMAS🤶🏻🎄 #CozyLittleChristmas vinyl + merch are available @ https://t.co/Sj97PCZxur now (plus an exclusive red vinyl bb for @amazon https://t.co/4OL9OtmdMl) https://t.co/qjtDmOHVX8	10448	1729	2021-11-01 17:49:35
3	1452745813847777280	Productive 37 so far 💅🏻 @TheEllenShow ✅ traded 🍄🧢 & made it ☔️ w @lukebryan ✅ raced to kindness w Orion Jean ✅ got a dose of optimism from @MichaelJWoodard to stay “hope full” ✅ danced like a couple million ppl were watching Celebrate my bday w/ me 🎂 check ur tv listings! https://t.co/aRhG1ILi0i	9559	1446	2021-10-25 21:16:07
4	1452669168659034113	Starting today Gap is donating $1 per @Spotify stream of my version of the song featured in the Gap ad - up to $100,000 USD - to support @baby2baby’s mission to provide basic essentials to children living in poverty across the country. https://t.co/cZYUWv0AkB to listen + help♥️	5580	1218	2021-10-25 16:11:33
5	1452668720602509313	From folding sweatshirts at the Santa Barbara Gap to 20 years later, starring in a Mark Romanek-directed holiday campaign ❄️ dreams do come true! And @Gap is also helping to make dreams come true for @baby2baby 👶🏼 https://t.co/VnGe37q5Ik	14153	2705	2021-10-25 16:09:46
6	1451409851557421056	. @MichaelJWoodard is at it again… his new song ✨hope full✨ is out now on @unsubrecords! Listen for some of that glass half full optimism 💚 https://t.co/9OT8NcxpgC https://t.co/OMssFZLOoW	4520	387	2021-10-22 04:47:29
7	1450479348331384842	year 20 is underway and mama ain’t here to play 💁🏼‍♀️ https://t.co/uI7NyOBnGC	59314	4750	2021-10-19 15:09:59
8	1448864443765452803	⚡️Time for the rest of Pikachu’s friends to lead the charge!⚡️The #Pokemon25 album is out now!! (PS it also features my friend @cynthialovely 🤗) Gotta listen to ‘em all! ϞϞ(๑⚈ ․̫ ⚈๑)∩ https://t.co/AA1PzthmDq https://t.co/HvUs5CLqTR	8775	1431	2021-10-15 04:12:56
9	1448044457295712257	not this poor baby https://t.co/OJUAIpXfV7	32241	2780	2021-10-12 21:54:36
10	1445785256326402059	So caught up in these golden tickets I forgot about these golden shoes… but if IG can go down for a whole day then I can do a #shoesdaywednesday once in a while 💁🏼‍♀️ @kpcollections @AmazonFashion https://t.co/RGgQLSSyTr	8308	613	2021-10-06 16:17:21
11	1443795730838134790	OMG @lorde IS A MOTHER F*CKING G	37540	3966	2021-10-01 04:31:41
12	1443278427146969091	Proud to be able to bring a little spotlight to my @fireworkfound and all its colors 🎇♥️ https://t.co/qnunmqqUJv	3994	462	2021-09-29 18:16:06
13	1443278249258151936	Thank you @Variety for honoring me with one of your #powerofwomen covers 🙏🏼 https://t.co/kwGznCftCb https://t.co/CbzUT9XBtI	20221	2845	2021-09-29 18:15:24
14	1442940012622843904	SPOTTED: one of my favorite @kpcollections sandals - get them❗️quick❗️for the last warm weather days of the yearrrrr 🌅👋🏻😩 #shoesdaytuesday @AmazonFashion https://t.co/6njUtH9lDs	9723	847	2021-09-28 19:51:22
15	1441417936149757970	Every song has a color. (What, you didn’t know that?!) Well, see them for yourself at https://t.co/jTl6ZumhKE. Then come back and tell me the color of your favorite song… 🎶 🎨 #MusicInColor #BEHRxSpotify @BehrPaint @Spotify https://t.co/wpBntYbhKI	14966	2878	2021-09-24 15:03:10
16	1440548677186035714	When #SMILE hits 1B and it’s also #shoesdaytuesday 🙂🙃 @kpcollections @AmazonFashion #latepost #stilltuesdayincalifornia https://t.co/hnfS9UoYNy	7636	622	2021-09-22 05:29:03
17	1440417281532522502	1 BILLION TOTAL @SPOTIFY STREAMS ON #SMILE TIME TO BRING OUT THE BIG BALLOONS ♥️♥️♥️ thanks KCs! https://t.co/5MRjFN3x5k	57127	7111	2021-09-21 20:46:56
18	1440151786380165128	You and iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiuiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii	96279	11419	2021-09-21 03:11:57
19	1438622553019322385	RT @Startboii1: You and iiiiiiii @katyperry 🌺 https://t.co/j6pU4ADLko	0	1463	2021-09-16 21:55:19
20	1438593568528994321	Motherhood lessons w/ @Oprah & @GayleKing: - Ostriches are shaped different than babies - Yrs of burrito eating will be good prep 🌯 - U have to brush their teeth even if they get new ones later 🦷 I promise itll all make sense (good luck @kirbybump) 👼 https://t.co/8f8Bz7c2V4 https://t.co/GrRsQERCMq	7560	981	2021-09-16 20:00:09
21	1437921958687956994	When HiH is having a mome 💞🌺 but it’s also #ShoesdayTuesday 😜#YouAndI aka @kpcollections and #KCs @AmazonFashion https://t.co/5zYkrmseJ5	14408	1965	2021-09-14 23:31:24
22	1437612359955070976	Share this if you’ve already voted NO on the California recall 💁🏼‍♀️ (or if you’ve reminded your fav California friendz n fam to vote! ✔️) PS 🚨YOU ONLY HAVE TIL 8PM TOMORROW!🚨 https://t.co/MZl8eBwHXL	7586	1299	2021-09-14 03:01:10
23	1437459028481744910	For too many kids, The Teenage Dream is shattered by school shootings. Gun violence shouldn’t be part of growing up. Please share this important PSA from @sandyhook #TheTeenageDream 💔 https://t.co/7q8JhK05s1	10720	2000	2021-09-13 16:51:53
24	1437250859235037188	stream Harley’s In Hawaii to feel like you’re on an all expenses paid vacation	48671	8269	2021-09-13 03:04:42
25	1436033184857149454	All board the Lane Train… LFG!!! @corral_matt for Heisman OK ☝🏻🌽🐕🙏🏻 https://t.co/vLEEpxmIb5	12015	2695	2021-09-09 18:26:06
26	1435457743159054337	Happy to be in such great company ♥️♥️♥️ Rita Moreno, Channing Dungey, Katy Perry, Amanda Gorman, Lorde to Be Honored at Variety’s Power of Women Event https://t.co/gf9FnWpTWe via @variety	7760	872	2021-09-08 04:19:30
27	1435398882758823937	@tylerthecreator i got u https://t.co/GgIeC5cXeC	1799	107	2021-09-08 00:25:36
28	1435343427214843904	Presenting The Flapper: when the only thing flat about you are the shoes on your feet 🥿 💁🏼‍♀️ #shoesdaytuesday @kpcollections @AmazonFashion https://t.co/mFIdpZ9abu	5111	404	2021-09-07 20:45:15
29	1433862229661679627	. @LuisaViaRoma: Vegas, @UNICEF, and @kpcollections, oh my! 🖤 https://t.co/GF2HedfdIq https://t.co/Eok156M3JX	17763	2691	2021-09-03 18:39:30
30	1433835886949462019	California, here is how to not turn into Texas 🤦🏼‍♀️ #VoteNoOnTheRecall 🗳 Go to your nearest mailbox, ballot drop box, or early voting location by September 14. That’s it 💅🏻 (for now) https://t.co/Eam6UUcYO3	7372	1108	2021-09-03 16:54:49
31	1433693640908414979	fear is the quickest way to lower your immunity system… JOY is the quickest way to STRENGTHEN your immunity system ♥️	25459	2858	2021-09-03 07:29:35
32	1432875609131126786	You’ve got one more week to officially show off that pedicure 💅🏻☀️ #summerisover #shoesdaytuesday @kpcollections @AmazonFashion https://t.co/PL3oaIKWha	4885	377	2021-09-01 01:19:01
33	1432856718447239176	dang #CarlSagan the prophet, an excerpt from his book from 1995: https://t.co/9f3crqXJjA	11701	1545	2021-09-01 00:03:57
34	1431680625535160331	perspective! https://t.co/nDWkqW9d9m	6540	529	2021-08-28 18:10:35
35	1431413104588451846	the people who are crazy enough to think they can change the world, are the ones who do	47665	5945	2021-08-28 00:27:33
36	1431376334199005188	❗️🚨 MSG FROM YOUR CALI GURL 🚨❗️ Don’t roll back the progress our great state has made on COVID, climate change, gun safety & healthcare ✅ Check 📬 for your ballot ✅ Vote #NoOnRecall ✅ Drop your ballot in the nearest mailbox, ballot drop box, or early voting location by 9/14 https://t.co/bQGmb9zot5	5602	892	2021-08-27 22:01:26
37	1430938955571101697	1 year ago today is the day my life began… Happy first Birthday my Daisy Dove, my love. ♥️	107922	9593	2021-08-26 17:03:27
38	1430322545610223617	Of course I took a heel and 💛🌼 COVERED IT IN DAISIES 🌼💛 #shoesdaytuesday @kpcollections @AmazonFashion https://t.co/gRNzObTol2	8968	622	2021-08-25 00:14:03
39	1430054751077425153	do you like turmeric 😂 https://t.co/SXlPe1UNPH	6421	446	2021-08-24 06:29:56

	tweet_id	tweets	likes	retweets	time
0	1465211314624622592	@KendrickPerkins You’re absolutely right, totally agree with you! Carrying on with this 🍷 in my hand as I type this.	13430	707	2021-11-29 06:49:34
1	1465120691343163401	Coolest thing ever!!! https://t.co/CbKdBDPbud	32800	2225	2021-11-29 00:49:28
2	1464272233036091396	RT @LJFamFoundation: 3-Pack I Promise Band drop?! 😱🤯 We’ve got over 30 different I Promise Band styles from @KingJames’ collection we’re dr…	0	85	2021-11-26 16:37:59
3	1463815236603940868	👑 https://t.co/K9Cjx59JjM	132673	12096	2021-11-25 10:22:03
4	1462145374785884166	Can somebody check in on my brother @Money23Green please! Haven’t heard from him today. 🤣🤣🤣. #GoBuckeyes🌰 OH…… @OhioStateFB	44631	2299	2021-11-20 19:46:37
5	1460751092334182402	RT @nikebasketball: "In the future, eventually I want to give back to my community just as LeBron has and be a big influence on the people…	0	145	2021-11-16 23:26:14
6	1460751016870268932	RT @nikebasketball: "Growing up in Northeast Ohio, you watch a lot of LeBron and that's who we aspire to be like. When you watch him on the…	0	304	2021-11-16 23:25:56
7	1460487931982856199	My brother forever!!! 🤜🏻🤛🏾❤️ https://t.co/Q75kG6w5ko	127822	6661	2021-11-16 06:00:32
8	1460487693280808968	🐐 https://t.co/EiYIVq0szr	61152	3890	2021-11-16 05:59:35
9	1460416408127303681	Purchased this vintage record(vinyl) player for the crib! Absolutely love it but it needs to be restored! Can anyone in LA help me out. It’s called *Clairtone g2 https://t.co/6k6EVtpT77	31637	1038	2021-11-16 01:16:19
10	1460047003979694081	🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣 https://t.co/0CWz9uxM0r	76343	5932	2021-11-15 00:48:26
11	1460045538468892679	🔥🔥🔥🔥🔥🔥🔥🚀🚀🚀🚀 https://t.co/U9JUBfDK3Z	17870	1156	2021-11-15 00:42:37
12	1459708769554763779	These are BEAUTIFULLY GORGEOUS man!!! 😍😍😍😍😍😍 https://t.co/9WuJdK7iZz	25552	1610	2021-11-14 02:24:25
13	1459708368487022595	So 🔥🔥🔥🔥🔥🔥🔥🔥!! Congrats Young King 🤴🏾 @Lj_era8 🙏🏾✊🏾❤️👑 https://t.co/mXZVbXWjKx	48694	3747	2021-11-14 02:22:49
14	1459707525830377474	Do what you do and turn that joint UP!!!!!! https://t.co/8q54PLB77j	2464	88	2021-11-14 02:19:28
15	1459331742997770241	@djmeel @81smallz Hahaha @ Smallz	108	6	2021-11-13 01:26:15
16	1459331575888244736	RT @Sean_Lyric: yo @KingJames, my student said he reppin’ hard! @spacejammovie @uninterrupted @TheSpringHillCo https://t.co/KRoZvyB74w	0	91	2021-11-13 01:25:35
17	1459331504136355845	RT @LJFamFoundation: Feeling AND looking good! 😍 We surprised our I Promise students at East CLC with their new #WeAreFamily gear to reward…	0	53	2021-11-13 01:25:18
18	1459331455247552513	RT @cg_knowsbb: My guy is all in with @KingJames and @IPROMISESchool at the book fair! 📚x🏀 ♾ #DadLife https://t.co/P1y82zsLgV	0	82	2021-11-13 01:25:06
19	1458966137681506312	Love this “ALL MADDEN DOC” short clip before TNF game! Can’t wait for Xmas to see the whole thing. Been a John Madden fan my whole life. And the game made it even GREATER!! “BOOM HE’S ON HIS BACK”	9660	395	2021-11-12 01:13:28
20	1458920502878093314	EXTREMELY HAPPY for Cam!! Back where it all started! Shine brother shine!! ✨✨🙏🏾✊🏾👑	47021	3416	2021-11-11 22:12:07
21	1458904128701272067	Welcome to LA my brother @obj! It’s GO TIME!!	96305	7790	2021-11-11 21:07:04
22	1458619464329670657	What tears????? I didn’t see one. Man knock it off! That boy ate some lemon heads before walking into court. 🤣🤣🤣 https://t.co/LKwYssIUmD	266574	39717	2021-11-11 02:15:54
23	1458138438348120067	RT @LJFamFoundation: From keeping a promise to graduate from high school to accepting a new challenge of graduating from college. 💪🏾👑 Marce…	0	85	2021-11-09 18:24:29
24	1458138428407648257	RT @LJFamFoundation: I Promise Seniors, this is for you! 🙏 @uakron has created the Zips I Promise Scholarship as an additional opportunity…	0	73	2021-11-09 18:24:26
25	1458137109949140994	🤣🤣🤣🤣🤣🤣. Real life “Step-Brothers” https://t.co/VpfM5w7ETw https://t.co/v2GVfhIP4G	5838	343	2021-11-09 18:19:12
26	1457972372514902018	*shoe	5077	135	2021-11-09 07:24:36
27	1457964813607333890	Facts!!!! That’s was absurd. That player if the show was on the other foot would have been thrown out the game, suspended, fined and depending on the player possibly CUT from the team. 🤦🏾‍♂️ https://t.co/72n3ZlmWg0	30734	2240	2021-11-09 06:54:33
28	1457515888169480193	Rubio going INSANE in the garden right now!!!!	68786	5066	2021-11-08 01:10:41
29	1457500719078989829	🐍💚🧡🙏🏾✊🏾👑. Let’s Goooooooo @FAMUAthletics 🗣🗣🗣🗣 https://t.co/ySKOjkCOF5	31048	3907	2021-11-08 00:10:25
30	1457438497506168833	@djstephfloss 🤣🤣🤣🤣🤣🤣🤣	296	6	2021-11-07 20:03:10
31	1457438446557876225	@BrysonAdair A flat out dog!!! Love him	143	9	2021-11-07 20:02:58
32	1457437733891162112	Man get these boys out of here!!!! #Browns	11506	502	2021-11-07 20:00:08
33	1457421927308410880	DPJ!!!! TD. LFG 🗣🗣🗣🗣	14117	531	2021-11-07 18:57:19
34	1457410675697807368	Pick 6 @denzelward!!! Yessir ✊🏾	12553	561	2021-11-07 18:12:37
35	1457108344343195652	RT @RomeTrav: Got a chance to visit the building named after my brother with my brothers. It was a surreal experience. Thanks my G @KingJam…	0	448	2021-11-06 22:11:15
36	1456417029540384788	Yessir!!!!!! Disruption at its finest. LFG! #Klutch https://t.co/43vDLTBnHh	4141	285	2021-11-05 00:24:13
37	1456256879663861766	Loved those books!!! Spooky 👻 https://t.co/Pl19U0KYj0	10456	533	2021-11-04 13:47:50
38	1456109458883375113	🩸line https://t.co/7IboEyNLdW	67992	4405	2021-11-04 04:02:02
39	1456053968082067456	Can watch him shoot the 🪨 ALL DAY LONG!! TOOOOOOO PURE https://t.co/bGIhZtgE51	97506	5821	2021-11-04 00:21:32

	tweet_id	tweets	likes	retweets	time
0	1465051800466300934	There is still a long road ahead and nothing is impossible when you play for Man. United. We will keep chasing our goals until the end! 👊🏽 https://t.co/FZEuoDqBVa	197073	12119	2021-11-28 20:15:43
1	1464012032181690377	@Pink Hi @Pink - am happy to help - I will organise some signed shirts for these kids no problem. Congratulations on making this happen!	112336	12546	2021-11-25 23:24:03
2	1462723282323615752	He’s been my striker when I first came to Old Trafford and he’s been my coach since I came back to Man. United. But most of all, Ole is an outstanding human being. I wish him the best in whatever his life has reserved for him. Good luck, my friend! You deserve it! https://t.co/pdm7RXr2RX	401859	32612	2021-11-22 10:03:01
3	1461729514749366281	Time to role up the sleeves once again and just get the job done! Let’s chase what we are trying to achieve this season! 💪🏽 #MUFC https://t.co/vgE4lYv8tv	201193	13703	2021-11-19 16:14:08
4	1459569995596374019	É por Portugal e pelos portugueses que queremos marcar, uma vez mais, presença no Campeonato do Mundo. E é com a força de Portugal e dos portugueses que vamos lá chegar! Contamos com o vosso apoio! Rumo ao Catar 2022! 🇵🇹🙏🏽💪🏽 https://t.co/dQ0emTNpql	139599	8324	2021-11-13 17:12:59
5	1459112205652213772	Muitos parabéns princesa do papá! Que tenhas um dia feliz meu amor! 🎂🎉🙏🏽❤️ https://t.co/tLyctccRst	273432	11938	2021-11-12 10:53:53
6	1458009811833036802	Bom dia 🇵🇹 É sempre uma felicidade extra quando estou de volta ao meu país! 🙏🏽 https://t.co/VgJq5ttGYB	194581	9246	2021-11-09 09:53:22
7	1456320589434556419	Play Kungfu Dash game by @zujugp - https://t.co/5Afk6fGVl0 Highest scorers win a chance to meet me online in a few weeks! Até Breve! #WritingTheFutureOfFootball #KungfuDash https://t.co/wCn3PmTeOf	44664	2518	2021-11-04 18:01:00
8	1455959037237997569	I fuel my performance with @Herbalife24 #CR7Drive 💧 https://t.co/6pJsHfONJ3	62487	4029	2021-11-03 18:04:19
9	1455671594119012352	𝐖𝐞 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐮𝐧𝐭𝐢𝐥 𝐭𝐡𝐞 𝐞𝐧𝐝… and we will do everything to achieve our goals! We are RED DEVILS 💪🏽 #MUFC #championsleague https://t.co/gqWduRwEjl	324537	29970	2021-11-02 23:02:07
10	1454522123700088834	𝑻𝒉𝒊𝒔 𝒊𝒔 𝑴𝒂𝒏. 𝑼𝒏𝒊𝒕𝒆𝒅 🔴⚪️⚫️💪🏽 https://t.co/JQnggntuQ4	440592	44365	2021-10-30 18:54:32
11	1453427252297883660	Hard work is the only pathway to victory. There are no shortcuts to glory. No pain, no gain! 💪🏽 https://t.co/v34i1e7amU	234104	15683	2021-10-27 18:23:54
12	1451148164199862275	𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝘀 𝗟𝗲𝗮𝗴𝘂𝗲 𝘃𝗶𝗯𝗲𝘀…🤷🏽‍♂️🙏🏽 https://t.co/ahHZCK1DWA	349954	26249	2021-10-21 11:27:38
13	1450937620993822723	Yes! The Theater Of Dreams is on fire! We are alive! We are Man. United and we never give up! This is Old Trafford! 🔴⚪️⚫️ 💪🏽 https://t.co/3rsmOBpS8H	422829	53309	2021-10-20 21:31:00
14	1449057732682715144	Proud to have #HerbalifeNutrition fueling me through my new journey. Sky is the limit. @Herbalife https://t.co/nYgYd7cINF	55845	3437	2021-10-15 17:01:00
15	1448921837094666249	Morning essentials: coffee and motivation. #CR7UNDERWEAR https://t.co/VQ1Db1j4Zv	209337	9279	2021-10-15 08:01:00
16	1448719931223248911	To all the fans who voted for me for Player Of The Month… Thanks, guys! 👏🏽🤜🏽🤛🏽 https://t.co/Nf9wnJzvXq	294394	21412	2021-10-14 18:38:42
17	1448436107734093831	I’m all in. Now you! @zujugp connects football communities from East to West. More here at https://t.co/MFLVkWn6V7 #WritingTheFutureOfFootball https://t.co/8KqKXRRPst	42723	3237	2021-10-13 23:50:53
18	1447321290667282437	Good news coming soon. Sign up @zujugp https://t.co/EidpSeKgoU and start this journey with us! #WritingTheFutureOfFootball https://t.co/mg79ozuKk5	84168	3550	2021-10-10 22:01:00
19	1447132142757195778	Os recordes são sempre marcantes, mas aqueles que caem ao serviço da nossa Selecção assumem uma dimensão especial. 181 jogos com a camisola das quinas são 181 motivos de orgulho! E marcar contra 46 países diferentes é algo que nunca imaginei que fosse possível.Força Portugal 🇵🇹💪🏽 https://t.co/egmX1U7PSo	200438	15096	2021-10-10 09:29:23
20	1446965936431448067	O mesmo orgulho de sempre na hora de representar o meu país e de marcar com as nossas cores. Mais um teste, mais um ensaio, seguimos juntos na ambição, no sonho e no rumo inamovível que traçámos: marcar presença no histórico Mundial do Catar em 2022! 🇵🇹💪🏽 https://t.co/2S1BjJRrSM	139903	9735	2021-10-09 22:28:57
21	1446462516767363103	Proud to be elected Player Of The Month among so many great players in the Premier League. Thanks to all my teammates at Man. United, couldn’t have done it without them. Let’s keep working hard and the results will come!🔴⚫️💪🏽 https://t.co/OFFmdyfJ7z	263218	26313	2021-10-08 13:08:32
22	1445453534573465609	De volta a casa 🇵🇹👏🏽 https://t.co/hraQLRppgl	262161	13036	2021-10-05 18:19:12
23	1443999159711715328	Being confident is part of success. Choose your path and own it with CR7 Game On! #CR7GAMEON https://t.co/JMUQRyA9va	172333	9350	2021-10-01 18:00:02
24	1443863506760130560	Add some irreverence to your Autumn outfits with the irresistible CR7 Eyewear! #CR7EYEWEAR https://t.co/VP73G7hche	217280	11250	2021-10-01 09:01:00
25	1439969349918502913	Together we’re stronger! Thank you all for your support! ALWAYS 👏🏽🔴⚫️💪🏽 #mufc https://t.co/MRgIUg0VUo	358519	24799	2021-09-20 15:07:01
26	1437894020060037121	Wasn't the result we wanted, but now it’s time to recover well and focus on the next game!👏🏽👊🏽 #mufc https://t.co/J97WdtIylR	283786	18960	2021-09-14 21:40:23
27	1434581767353163779	Thank you to the Guiness World Records. Always good to be recognized as a world record breaker. Let’s keep trying to set the numbers even higher!💪🏽 https://t.co/qswCiXnRGh	458603	32802	2021-09-05 18:18:41
28	1433454907353411586	Another historic night! Unforgettable 🙏🏽 111⚽️🤭 https://t.co/Eq4ko7bHfP	692099	64610	2021-09-02 15:40:56
29	1433338907702222848	Not just another record broken, but another record earned. I think it is really important, whatever it is that you want to achieve in life, that you set goals, so you have something to work towards. I hope this goal will help you reach your greatness. #nike #nikefootball https://t.co/i2MQ7dsfiE	205264	27973	2021-09-02 08:00:00
30	1432429902209372166	É sempre um orgulho voltar à seleção e representar o nosso país!🇵🇹🙏🏽 https://t.co/YUvX8Zunyg	333316	17132	2021-08-30 19:47:56
31	1432251744508014592	One goal can change everything #morethanascore @livescore https://t.co/cb5fxiXUdy	122386	7526	2021-08-30 08:00:00
32	1430938339658502144	My home town, Madeira island, has been nominated for several awards by the World Travel Awards, I count on you to vote😉: https://t.co/C4neXrazzy; https://t.co/fxhiCZ6QOn; https://t.co/9xWtKJkAkC; https://t.co/fsnAQ5C4Cu @madeira_islands https://t.co/jRRPoQlRUq	108438	6695	2021-08-26 17:01:00
33	1430440055953235980	Start the day with a fragrance that matches your style! #CR7PLAYITCOOL https://t.co/03WKfnHcJn	264781	15156	2021-08-25 08:01:00
34	1426103220355670016	This one was fun to make. And so true #MoreThanAScore https://t.co/HPbuALZCzH	134807	11162	2021-08-13 08:47:58
35	1425785066043715586	👀⚽️🥅💪🏽 https://t.co/MXxJSztxmy	394221	16819	2021-08-12 11:43:44
36	1424661515764895750	keep working 💪🏽 https://t.co/h6Zok4S6HJ	413571	17870	2021-08-09 09:19:08
37	1422982847682555910	Don't give up on the dream of graduating! Take part in the selection for the 24 eCampus scholarships that I am happy to donate. Send your application by 12 September on https://t.co/gmrmBy6oey. I will be proud to support your studies! @uni_ecampus https://t.co/NAf5IHosEh	84398	8098	2021-08-04 18:08:43
38	1420392465018855425	Felice di tornare al lavoro ⚽️💪🏽 https://t.co/qgSjgpgpbS	263956	12374	2021-07-28 14:35:27
39	1418257821041414145	My beautiful queen ❤️ https://t.co/Jmbld6ySq7	495141	19909	2021-07-22 17:13:09

	tweet_id	tweets	likes	retweets	time
0	1459731059353042945	GOREBELS HOTTYTODDY	20079	2982	2021-11-14 03:52:59
1	1457915007245570052	daylight savings is daytime terror for parents latetweet butstillrelevant	14300	1172	2021-11-09 03:36:39
2	1455230552957591553	I mean, justice for Thanksgiving, but if ur already into the winter wonderland vibes why not make it a 🎄🤶🏻VERY PERRY CHRISTMAS🤶🏻🎄 CozyLittleChristmas vinyl + merch are available @ now (plus an exclusive red vinyl bb for @amazon	10448	1729	2021-11-01 17:49:35
3	1452745813847777280	Productive 37 so far 💅🏻 @TheEllenShow ✅ traded 🍄🧢 & made it ☔️ w @lukebryan✅ raced to kindness w Orion Jean✅ got a dose of optimism from @MichaelJWoodard to stay “hope full”✅ danced like a couple million ppl were watchingCelebrate my bday w/ me 🎂 check ur tv listings!	9559	1446	2021-10-25 21:16:07
4	1452669168659034113	Starting today Gap is donating $1 per @Spotify stream of my version of the song featured in the Gap ad - up to $100,000 USD - to support @baby2baby’s mission to provide basic essentials to children living in poverty across the country. to listen + help♥️	5580	1218	2021-10-25 16:11:33
5	1452668720602509313	From folding sweatshirts at the Santa Barbara Gap to 20 years later, starring in a Mark Romanek-directed holiday campaign ❄️ dreams do come true! And @Gap is also helping to make dreams come true for @baby2baby 👶🏼	14153	2705	2021-10-25 16:09:46
6	1451409851557421056	. @MichaelJWoodard is at it again… his new song ✨hope full✨ is out now on @unsubrecords! Listen for some of that glass half full optimism 💚	4520	387	2021-10-22 04:47:29
7	1450479348331384842	year 20 is underway and mama ain’t here to play 💁🏼‍♀️	59314	4750	2021-10-19 15:09:59
8	1448864443765452803	⚡️Time for the rest of Pikachu’s friends to lead the charge!⚡️The Pokemon25 album is out now!! (PS it also features my friend @cynthialovely 🤗) Gotta listen to ‘em all! ϞϞ(๑⚈ ․̫ ⚈๑)∩	8775	1431	2021-10-15 04:12:56
9	1448044457295712257	not this poor baby	32241	2780	2021-10-12 21:54:36
10	1445785256326402059	So caught up in these golden tickets I forgot about these golden shoes… but if IG can go down for a whole day then I can do a shoesdaywednesday once in a while 💁🏼‍♀️ @kpcollections @AmazonFashion	8308	613	2021-10-06 16:17:21
11	1443795730838134790	OMG @lorde IS A MOTHER F*CKING G	37540	3966	2021-10-01 04:31:41
12	1443278427146969091	Proud to be able to bring a little spotlight to my @fireworkfound and all its colors 🎇♥️	3994	462	2021-09-29 18:16:06
13	1443278249258151936	Thank you @Variety for honoring me with one of your powerofwomen covers 🙏🏼	20221	2845	2021-09-29 18:15:24
14	1442940012622843904	SPOTTED: one of my favorite @kpcollections sandals - get them❗️quick❗️for the last warm weather days of the yearrrrr 🌅👋🏻😩 shoesdaytuesday @AmazonFashion	9723	847	2021-09-28 19:51:22
15	1441417936149757970	Every song has a color. (What, you didn’t know that?!)Well, see them for yourself at Then come back and tell me the color of your favorite song… 🎶 🎨 MusicInColor BEHRxSpotify @BehrPaint @Spotify	14966	2878	2021-09-24 15:03:10
16	1440548677186035714	When SMILE hits 1B and it’s also shoesdaytuesday 🙂🙃 @kpcollections @AmazonFashion latepost stilltuesdayincalifornia	7636	622	2021-09-22 05:29:03
17	1440417281532522502	1 BILLION TOTAL @SPOTIFY STREAMS ON SMILE TIME TO BRING OUT THE BIG BALLOONS ♥️♥️♥️ thanks KCs!	57127	7111	2021-09-21 20:46:56
18	1440151786380165128	You and iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiuiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii	96279	11419	2021-09-21 03:11:57
19	1438593568528994321	Motherhood lessons w/ @Oprah & @GayleKing:- Ostriches are shaped different than babies - Yrs of burrito eating will be good prep 🌯 - U have to brush their teeth even if they get new ones later 🦷 I promise itll all make sense (good luck @kirbybump) 👼	7560	981	2021-09-16 20:00:09
20	1437921958687956994	When HiH is having a mome 💞🌺 but it’s also ShoesdayTuesday 😜YouAndI aka @kpcollections and KCs @AmazonFashion	14408	1965	2021-09-14 23:31:24
21	1437612359955070976	Share this if you’ve already voted NO on the California recall 💁🏼‍♀️ (or if you’ve reminded your fav California friendz n fam to vote! ✔️)PS 🚨YOU ONLY HAVE TIL 8PM TOMORROW!🚨	7586	1299	2021-09-14 03:01:10
22	1437459028481744910	For too many kids, The Teenage Dream is shattered by school shootings. Gun violence shouldn’t be part of growing up. Please share this important PSA from @sandyhook TheTeenageDream 💔	10720	2000	2021-09-13 16:51:53
23	1437250859235037188	stream Harley’s In Hawaii to feel like you’re on an all expenses paid vacation	48671	8269	2021-09-13 03:04:42
24	1436033184857149454	All board the Lane Train… LFG!!! @corral_matt for Heisman OK ☝🏻🌽🐕🙏🏻	12015	2695	2021-09-09 18:26:06
25	1435457743159054337	Happy to be in such great company ♥️♥️♥️Rita Moreno, Channing Dungey, Katy Perry, Amanda Gorman, Lorde to Be Honored at Variety’s Power of Women Event via @variety	7760	872	2021-09-08 04:19:30
26	1435398882758823937	@tylerthecreator i got u	1799	107	2021-09-08 00:25:36
27	1435343427214843904	Presenting The Flapper: when the only thing flat about you are the shoes on your feet 🥿 💁🏼‍♀️ shoesdaytuesday @kpcollections @AmazonFashion	5111	404	2021-09-07 20:45:15
28	1433862229661679627	. @LuisaViaRoma: Vegas, @UNICEF, and @kpcollections, oh my! 🖤	17763	2691	2021-09-03 18:39:30
29	1433835886949462019	California, here is how to not turn into Texas 🤦🏼‍♀️VoteNoOnTheRecall 🗳Go to your nearest mailbox, ballot drop box, or early voting location by September 14.That’s it 💅🏻 (for now)	7372	1108	2021-09-03 16:54:49
30	1433693640908414979	fear is the quickest way to lower your immunity system… JOY is the quickest way to STRENGTHEN your immunity system ♥️	25459	2858	2021-09-03 07:29:35
31	1432875609131126786	You’ve got one more week to officially show off that pedicure 💅🏻☀️ summerisover shoesdaytuesday @kpcollections @AmazonFashion	4885	377	2021-09-01 01:19:01
32	1432856718447239176	dang CarlSagan the prophet, an excerpt from his book from 1995:	11701	1545	2021-09-01 00:03:57
33	1431680625535160331	perspective!	6540	529	2021-08-28 18:10:35
34	1431413104588451846	the people who are crazy enough to think they can change the world, are the ones who do	47665	5945	2021-08-28 00:27:33
35	1431376334199005188	❗️🚨 MSG FROM YOUR CALI GURL 🚨❗️Don’t roll back the progress our great state has made on COVID, climate change, gun safety & healthcare✅ Check 📬 for your ballot✅ Vote NoOnRecall✅ Drop your ballot in the nearest mailbox, ballot drop box, or early voting location by 9/14	5602	892	2021-08-27 22:01:26
36	1430938955571101697	1 year ago today is the day my life began… Happy first Birthday my Daisy Dove, my love. ♥️	107922	9593	2021-08-26 17:03:27
37	1430322545610223617	Of course I took a heel and 💛🌼 COVERED IT IN DAISIES 🌼💛 shoesdaytuesday @kpcollections @AmazonFashion	8968	622	2021-08-25 00:14:03
38	1430054751077425153	do you like turmeric 😂	6421	446	2021-08-24 06:29:56
39	1429949299572449282	infodemic > pandemic	25756	2102	2021-08-23 23:30:54

	ID	Screen_Name	Name	Created_At	Favourites_Count	Followers_Count	Following_Count	Following	Listed_Count	Verified
0	987764098375938048	fireworkfound	Firework Foundation	2018-04-21 18:44:47	7	550	7	False	1	False
1	6585222	Moonalice	Roger McNamee	2007-06-05 04:15:25	107439	83376	2424	False	1173	False
2	14075928	TheOnion	The Onion	2008-03-04 02:48:37	3	11701827	8	False	68412	True
3	18194194	feraljokes	Jake Flores	2008-12-17 17:37:41	184838	42141	3116	False	347	True
4	289548939	bariweiss	Bari Weiss	2011-04-28 21:03:26	29495	382900	3483	False	2516	True
5	1314604885	AlbertEinstein	Albert Einstein	2013-03-29 17:37:48	3324	295401	1238	False	1234	True
6	3092541736	MorningBrew	Morning Brew ☕️	2015-03-16 18:43:25	16454	304150	1591	False	2071	True
7	25422302	Liv_Boeree	Liv Boeree	2009-03-20 00:40:42	13072	182474	401	False	2154	True
8	27854374	seiff	ken seiff	2009-03-31 11:48:18	684	2041	410	False	39	False
9	3764301	RyanHoliday	Ryan Holiday	2007-04-08 05:45:46	2194	405501	845	False	4694	True
10	80638910	JessicaYellin	Jessica Yellin	2009-10-07 18:01:50	4113	111506	6753	False	1770	True
11	17995040	benshapiro	Ben Shapiro	2008-12-09 16:40:22	1	3698230	310	False	12377	True
12	14130366	sundarpichai	Sundar Pichai	2008-03-12 05:51:53	920	4080575	359	False	8264	True
13	15506669	JeffBezos	Jeff Bezos	2008-07-20 22:38:36	2	3283509	25	False	9358	True
14	11744152	yashar	Yashar Ali 🐘	2008-01-02 06:55:39	167472	752056	28760	False	5514	True
15	2314055840	mayaharris_	Maya Harris	2014-01-27 17:57:56	13603	475950	1010	False	1124	True
16	239509917	MsPackyetti	brittany packnett cunningham.	2011-01-17 19:58:52	63217	430781	4120	False	3001	True
17	259725229	ValaAfshar	Vala Afshar	2011-03-02 13:31:29	3	612873	300	False	11627	True
18	3235693280	sgi_info	Soka Gakkai Official	2015-06-04 04:29:45	466	15874	22	False	81	True
19	17248121	physorg_com	Phys.org	2008-11-08 08:54:53	16648	905313	158	False	12122	True
20	5402612	BBCBreaking	BBC Breaking News	2007-04-22 14:42:37	0	48516964	3	True	143403	True
21	18208354	joerogan	Joe Rogan	2008-12-18 04:01:47	904	7703063	3872	False	21751	True
22	1282121312	waitbutwhy	Tim Urban	2013-03-20 02:43:48	1747	394633	679	False	4088	True
23	17471979	NatGeo	National Geographic	2008-11-18 21:28:10	7574	26641601	179	True	64660	True
24	37710752	BillNye	Bill Nye	2009-05-04 17:42:04	154	6121785	132	False	20180	True
25	34743251	SpaceX	SpaceX	2009-04-23 21:53:30	360	18987232	100	False	33161	True
26	116994659	SamHarrisOrg	Sam Harris	2010-02-24 06:05:40	4001	1519722	631	False	11430	True
27	11417802	jgebbia	Joe Gebbia	2007-12-21 22:21:53	4040	102899	1003	False	1718	True
28	939091	JoeBiden	Joe Biden	2007-03-11 17:51:24	20	31786270	48	False	37600	True
29	180535644	esglaude	Eddie S. Glaude Jr.	2010-08-19 21:46:10	13653	334019	1718	False	2136	True
30	4185877812	SachaBaronCohen	Sacha Baron Cohen	2015-11-10 03:13:00	28	787698	13	False	1571	True
31	3564127636	70s_party	70s Dinner Party	2015-09-06 07:55:06	10147	171248	587	False	873	False
32	21265120	KillerMike	Killer Mike	2009-02-19 01:44:57	4508	564350	2051	False	2703	True
33	111909054	JOSH_BENNY	SAFDIE	2010-02-06 15:19:34	20406	119328	823	False	346	True
34	882710790196916224	whoacity	Whoa City	2017-07-05 21:20:27	22359	107404	103	False	294	False
35	1131633060078706689	BraggLiveFoods	Bragg Live Food Products, LLC	2019-05-23 18:48:41	1318	3295	236	False	29	False
36	887946728	chunkled	ᑕᕼᑌᑎK	2012-10-18 02:20:11	169716	28641	1289	False	121	False
37	14321959	umairh	❤️ Umair	2008-04-07 11:32:29	4627	229537	1556	False	6189	True
38	226222147	PeteButtigieg	Pete Buttigieg	2010-12-13 16:49:04	1750	3568737	2793	False	10309	True
39	16129920	maddow	Rachel Maddow MSNBC	2008-09-04 15:02:12	40	10579155	4529	False	51817	True
40	149763	sriramk	Sriram Krishnan - sriramk.eth	2006-12-22 06:29:50	106661	112078	1238	False	1998	True
41	452507479	RickRubin	Rick Rubin	2012-01-01 23:03:16	0	150151	0	False	679	True
42	1245320090	dubstep4dads	eric curtin	2013-03-06 05:18:15	77839	389393	1990	False	1608	True
43	307434638	AndrewPaulJoyce	Andrew Joyce	2011-05-29 16:43:14	13759	3809	2843	False	72	True
44	1093262898976022529	jlloydharmon	Jeremiah Lloyd Harmon	2019-02-06 21:39:21	504	22809	72	False	44	True
45	568545739	DojaCat	peepoop	2012-05-01 21:14:18	8809	3877993	0	False	1877	True
46	916718038585610242	MaddiePoppe	Maddie Poppe	2017-10-07 17:33:06	13165	85974	279	False	119	False
47	965266179836252161	callmeX	X González	2018-02-18 16:46:05	5340	1469102	259	False	3414	True
48	48289662	UniofOxford	University of Oxford	2009-06-18 08:28:28	15319	778059	1390	False	5122	True
49	52551600	JimCarrey	Jim Carrey	2009-06-30 22:58:44	8	18647911	1	False	53471	True
50	62988756	AmericanIdol	American Idol	2009-08-05 01:16:29	43346	1948668	5331	False	5705	True
51	17074714	lukebryan	Luke Bryan	2008-10-30 21:14:38	4756	9400994	10521	False	6008	True
52	188370588	alicewalkerfilm	Alice Walker Film	2010-09-08 15:35:53	43	8903	1699	False	203	False
53	43192807	Pixar	Pixar	2009-05-28 20:47:33	114	11809610	154	False	24494	True
54	14982804	ava	Ava DuVernay	2008-06-02 17:53:54	206304	2732206	16004	False	5905	True
55	48269483	GloriaSteinem	Gloria Steinem	2009-06-18 06:29:26	61	364163	156	False	3977	True
56	500704345	Pontifex	Pope Francis	2012-02-23 11:38:07	0	18825658	8	False	24050	True
57	17228366	LionelRichie	Lionel Richie	2008-11-07 08:18:07	2350	340119	57	False	2096	True
58	144400453	VCRebecca	Rebecca Lynn	2010-05-16 05:29:04	70	36097	1079	False	1305	False
59	28406270	kumailn	Kumail Nanjiani	2009-04-02 19:56:22	19468	3155185	3414	False	8476	True
60	78366205	TiffanyHaddish	Tiffany Haddish	2009-09-29 17:24:27	16439	575409	1320	False	1367	True
61	939718118850543616	SB_Updater	SB_Updater	2017-12-10 04:47:13	3208	2647	66	False	64	False
62	11348282	NASA	NASA	2007-12-19 20:20:32	13419	50041977	178	True	96983	True
63	346276932	TaraBrach	Tara Brach	2011-08-01 00:58:35	2470	89736	425	False	1520	False
64	19725644	neiltyson	Neil deGrasse Tyson	2009-01-29 18:40:26	6	14551703	39	False	45373	True
65	52544275	IvankaTrump	Ivanka Trump	2009-06-30 22:32:03	3939	10133800	1547	False	23792	True
66	1652541	Reuters	Reuters	2007-03-20 17:46:05	752	24036852	1196	False	129892	True
67	51241574	AP	The Associated Press	2009-06-26 21:48:52	3660	15385771	6845	False	101940	True
68	29450962	repjohnlewis	John Lewis	2009-04-07 13:49:52	1971	1201360	145	False	0	False
69	63302020	JordanPeele	Jordan Peele	2009-08-05 23:56:12	17767	1892770	1699	False	4736	True
70	181572333	chancetherapper	Chance The Rapper	2010-08-22 14:51:53	19144	8144090	3100	False	4965	True
71	757303975	ChelseaClinton	Chelsea Clinton	2012-08-14 14:55:27	28468	2968564	936	False	9461	True
72	21288052	zanelowe	Zane Lowe	2009-02-19 10:23:50	3085	679263	895	False	3483	True
73	88975905	shanesmith30	shane smith	2009-11-10 17:27:44	5572	155284	279	False	1483	True
74	3327720838	brielarson	Brie Larson	2015-06-15 18:32:45	13908	2747360	210	False	4031	True
75	82455213	RuPaul	RuPaul	2009-10-14 20:56:32	394	1507788	729	False	4703	True
76	39364684	chrissyteigen	chrissy teigen	2009-05-11 22:50:04	36692	13379226	4799	False	8856	True
77	1923827275	SkipMarley	Skip Marley	2013-10-01 15:34:20	1600	80915	109	False	137	True
78	29417304	deray	deray	2009-04-07 09:38:56	66847	1036360	1315	False	6581	True
79	531561605	AmericaFerrera	America Ferrera	2012-03-20 19:22:36	5442	247802	358	False	1303	True
80	796974685288157184	iwillharness	Harness	2016-11-11 07:15:46	2639	5469	378	False	43	False
81	784152188092190720	kpcollections	Katy Perry Shoes	2016-10-06 22:03:45	3719	29047	27	False	42	False
82	88749392	JuddApatow	Judd Apatow	2009-11-09 20:16:05	41063	2430733	1745	False	11015	True
83	800942537083068416	womensmarch	Women's March	2016-11-22 06:02:36	21191	624904	856	False	3396	True
84	20225875	i_D	i-D	2009-02-06 09:43:22	3008	1502936	745	False	8408	True
85	1917826916	maggierogers	Maggie Rogers	2013-09-29 18:33:14	25973	159455	491	False	449	True
86	15808765	CoryBooker	Cory Booker	2008-08-11 14:41:16	1017	4909999	88097	False	19441	True
87	29442313	SenSanders	Bernie Sanders	2009-04-07 13:02:35	365	12297924	2327	False	27797	True
88	970207298	SenWarren	Elizabeth Warren	2012-11-25 15:14:27	18	6987494	515	False	19716	True
89	71350663	JenniKonner	Jenni Konner	2009-09-03 20:27:57	22916	96764	1576	False	641	True
90	139162440	pattonoswalt	Patton Oswalt	2010-05-01 19:00:10	49498	4771413	4325	False	25216	True
91	131497030	VanJones68	Van Jones	2010-04-10 12:59:58	30111	1050161	10514	False	8392	True
92	15956067	joseiswriting	Jose Antonio Vargas	2008-08-23 11:18:35	22419	106113	20593	False	2319	True
93	30354991	KamalaHarris	Kamala Harris	2009-04-11 00:42:07	363	19037942	732	False	26401	True
94	13298072	Tesla	Tesla	2008-02-10 01:12:32	3918	11864993	79	False	18981	True
95	45709328	halsey	h	2009-06-08 23:58:35	5296	14524800	3042	False	6917	True
96	15251613	Popjustice	Peter Robinson	2008-06-27 04:08:08	4427	220316	1817	False	2250	True
97	17565514	shondarhimes	shonda rhimes	2008-11-23 00:06:22	3747	1987245	1619	False	7715	True
98	174110843	jonlovett	Jon Lovett	2010-08-03 03:41:01	6056	866318	1812	False	7237	True
99	124003770	cher	Cher	2010-03-17 23:05:55	1947	3948050	192	False	11711	True
100	19877186	chelseahandler	Chelsea Handler	2009-02-01 21:13:36	8458	7856745	631	False	30323	True
101	44196397	elonmusk	Elon Musk	2009-06-02 20:12:29	11021	64969380	104	False	81802	True
102	139823781	BetteMidler	bettemidler	2010-05-03 20:00:36	912	2041761	68	False	8128	True
103	30602016	iJesseWilliams	jesse Williams.	2009-04-12 04:37:25	5807	2378738	3450	False	0	True
104	46751744	UNDEADTRESOR	Enrique Shockwave	2009-06-12 21:21:05	119459	43188	1544	False	0	False
105	87215756	JackGarratt	Jack Garratt	2009-11-03 15:36:43	3881	68371	491	False	273	True
106	11094232	eldsjal	Daniel Ek	2007-12-12 16:39:54	1565	223988	690	False	2713	True
107	18978610	sallykohn	Sally Kohn	2009-01-14 14:56:16	36750	116007	7875	False	2488	True
108	11912362	WORLDSTAR	WORLDSTARHIPHOP	2008-01-06 18:06:13	247	5572199	3009	False	4271	True
109	223047786	joanneprada	Joanne The Scammer	2010-12-05 06:43:36	8223	525946	8637	False	571	True
110	30791425	cynthialovely	Cyn 🤍	2009-04-13 02:57:21	56870	26924	547	False	92	True
111	19719380	AAUW	AAUW	2009-01-29 16:44:35	34480	53110	14631	False	1557	True
112	1159251	shervin	Shervin Pishevar	2007-03-14 14:25:11	25067	109850	6688	False	3039	True
113	100220864	BrunoMars	Bruno Mars	2009-12-29 13:07:04	569	43171309	95	False	34081	True
114	2896348639	kpfragrances	Katy Perry Fragrance	2014-11-11 08:16:53	4974	43715	158	False	61	True
115	238319766	chrisrock	Chris Rock	2011-01-14 22:11:56	452	5220922	277	False	12268	True
116	22107140	getglucky	GLUCKY	2009-02-27 04:30:43	3426	14947	268	False	64	False
117	142118056	mkives	Michael Kives	2010-05-10 01:08:56	13877	5742	765	False	21	False
118	1339835893	HillaryClinton	Hillary Clinton	2013-04-09 18:04:35	1173	31037905	947	False	42294	True
119	25142830	DukeDumont	DukeDumont	2009-03-18 20:10:29	14101	144024	3128	False	692	True
120	25084660	arzE	Ezra Koenig	2009-03-18 14:52:55	9216	487982	967	False	0	True
121	9695312	billboard	billboard	2007-10-25 21:33:23	6911	12890131	3396	False	21315	True
122	47139630	petracollins	petra collins	2009-06-14 18:06:28	2625	31673	150	False	151	False
123	338674715	FuckJerry	Elliot Tebele	2011-07-19 22:49:55	766	82768	379	False	265	True
124	103012173	charli_xcx	Charli	2010-01-08 15:35:15	6328	3608552	888	False	3141	True
125	1630896181	VICENews	VICE News	2013-07-29 17:40:09	4796	1016833	965	False	11195	True
126	19279990	donaldglover	donald	2009-01-21 07:32:47	0	2583033	0	False	12394	True
127	22957878	mkmcguane	maggie mcguane	2009-03-05 18:44:08	194	8683	101	False	16	False
128	59949396	AnnaKendrick47	Anna Kendrick	2009-07-25 01:57:57	1233	7034046	343	False	17673	True
129	457554412	samsmith	samsmith	2012-01-07 15:05:53	38995	8004232	1302	False	4420	True
130	89898430	jennyslate	jenny slate	2009-11-14 08:16:42	6271	518476	606	False	2398	True
131	27778893	nicolerichie	Nicole Richie	2009-03-31 01:16:52	384	4287152	12033	False	18669	True
132	111501462	alliex	allie x	2010-02-05 04:16:11	9479	79992	1810	False	249	True
133	826762334	KeoNovak	Keough Novak	2012-09-16 08:42:04	1701	27229	215	False	68	False
134	1515629996	AwkwardAya	aya tanimura 🐙	2013-06-14 07:23:56	23210	21096	277	False	48	True
135	23544596	mindykaling	Mindy Kaling	2009-03-10 02:29:28	32436	11618702	933	False	22075	True
136	206541722	yaoxiaoart	Yao Xiao 要烧	2010-10-23 05:10:00	11362	15276	1640	False	173	True
137	20565284	elliegoulding	Ellie Goulding	2009-02-11 02:15:58	3752	6407533	608	False	8234	True
138	355307031	lorde	Lorde	2011-08-15 04:30:08	1593	7246588	100	False	6325	True
139	22338630	rickyvanveen	Ricky Van Veen	2009-03-01 06:29:18	47	19459	171	False	193	False
140	345808170	ScorpioMystique	ScorpioMystique	2011-07-31 06:11:55	36830	216179	20704	False	614	False
141	153694176	IGGYAZALEA	IGGY AZALEA	2010-06-09 06:10:41	39651	7695748	1447	False	5850	True
142	16190898	RyanSeacrest	Ryan Seacrest	2008-09-08 19:55:09	1486	15035619	586	False	34780	True
143	34507480	ArianaGrande	Ariana Grande	2009-04-23 02:56:31	43876	85016034	55586	False	55639	True
144	136361303	voguemagazine	Vogue Magazine	2010-04-23 18:33:32	5398	14463900	461	False	22294	True
145	16409683	britneyspears	Britney Spears	2008-09-22 20:47:35	2527	55724369	365920	False	109692	True
146	23497233	Sia	sia	2009-03-09 20:40:56	1404	3756705	358	False	6353	True
147	276540738	Grimezsz	𝔊𝔯𝔦𝔪𝔢𝔰 🐉 ᚷᚱᛁᛗᛖᛋ	2011-04-03 15:32:54	10389	1236975	1110	False	3131	True
148	140049744	charlottes_web	Charlotte Olympia	2010-05-04 12:27:02	252	40408	236	False	333	False
149	30925378	KaceyMusgraves	K A C E Y	2009-04-13 18:38:38	21831	976560	324	False	2002	True
150	369206130	SarahSoWitty	Sarah Schechter	2011-09-06 23:33:44	45191	34905	755	False	196	True
151	31080039	lenadunham	Lena Dunham	2009-04-14 07:35:38	22337	5272295	782	False	9407	True
152	18156146	sofifii	Sophia Rivka Rossi	2008-12-16 04:35:10	51712	149761	1890	False	1139	True
153	15846407	TheEllenShow	Ellen DeGeneres	2008-08-14 03:50:42	4417	77733384	26514	False	96862	True
154	27195114	Drake	Drizzy	2009-03-28 07:17:46	59	39356830	607	False	36770	True
155	27022632	rejectedjokes	Ben Schwartz	2009-03-27 15:10:15	24424	1273111	951	False	3760	True
156	562381032	iamrashidajones	Rashida Jones	2012-04-24 20:00:05	258	1057863	399	False	4192	True
157	340613443	RonanFarrow	Ronan Farrow	2011-07-23 00:15:26	16571	1123510	5122	False	6346	True
158	632281318	FREE_FACTS	📖🧠	2012-07-10 20:13:12	39	58825	3	False	307	False
159	128605530	jocandraw	Jocandraw	2010-04-01 16:38:41	1991	11711	324	False	51	False
160	20814270	TheRealFafi	The Fafster	2009-02-13 22:40:44	1276	19493	436	False	209	False
161	14824849	SteveMartinToGo	Steve Martin	2008-05-18 21:33:55	811	8704683	385	False	41500	True
162	53233433	BryanGaw	BryanBadass	2009-07-03 00:04:49	449	14775	185	False	29	False
163	28994993	MyBrewTube	CRAIG BREWER	2009-04-05 14:28:34	1259	50029	1461	False	420	True
164	43950575	jamesbvalentine	James Valentine	2009-06-01 19:24:14	5091	160369	648	False	988	True
165	122793469	ChrisAnokute	CHRIS ANOKUTE	2010-03-13 23:09:07	4567	22145	436	False	69	False
166	113514650	tavitulle	Tavi Gevinson	2010-02-12 01:46:04	4914	343739	1672	False	2387	True
167	14790966	DollyParton	Dolly Parton	2008-05-15 20:00:06	1002	5316813	45	False	14909	True
168	506814264	ByJakeBailey	Jake Bailey	2012-02-28 03:36:52	4	33019	394	False	70	False
169	42384799	MichaelRunion	michaelrunion	2009-05-25 10:31:05	1233	10790	955	False	50	False
170	18625669	CalvinHarris	Calvin Harris	2009-01-05 06:17:35	3015	11951852	3062	False	12629	True
171	18681139	priyankachopra	PRIYANKA	2009-01-06 16:05:15	1904	27510290	542	False	28072	True
172	512700138	Madonna	Madonna	2012-03-03 02:37:57	47	2737827	45	False	5880	True
173	79759935	amediumformat	Aaron Stern	2009-10-04 15:47:45	22392	13566	588	False	29	False
174	171960289	womenoftmrw	Women of Tomorrow	2010-07-28 15:48:58	1	12785	71	False	50	False
175	23375688	selenagomez	Selena Gomez	2009-03-09 00:16:45	2027	65381636	1080	False	121039	True
176	70915224	dizzycoco	Julie Turner	2009-09-02 08:25:17	12378	16591	695	False	34	False
177	21727835	TrickyStewart	Tricky Stewart	2009-02-24 04:07:16	146	48118	384	False	385	True
178	268414482	MileyCyrus	Miley Ray Cyrus	2011-03-18 18:36:02	1341	46435604	395	False	52538	True
179	141116825	Lockoir	Lockhart Brownlie	2010-05-07 06:39:38	125	31630	274	False	102	False
180	23737582	druidDUDE	Kevin McHale	2009-03-11 08:17:50	39917	1316215	835	False	11295	True
181	289759808	KathyBethTerry	Kathy Beth Terry	2011-04-29 05:01:27	3	105986	40	False	604	False
182	19764603	OHLANDmusic	OHLAND	2009-01-30 12:09:17	1354	88008	277	False	545	False
183	36008570	JessieJ	Jessie J	2009-04-28 06:34:34	1523	8156958	158	False	12147	True
184	32049158	Scott_Myrick	Scott Myrick	2009-04-16 18:12:23	2050	42141	191	False	98	False
185	275934616	CaseyHoops	Casey Hooper	2011-04-02 09:08:05	1310	23010	164	False	59	False
186	169686021	kanyewest	ye	2010-07-22 23:00:05	7	30622594	211	False	43403	True
187	189734996	onlyLeah	Leah	2010-09-12 02:34:26	2437	48913	133	False	151	False
188	30686649	MarinaDiamandis	Marina	2009-04-12 17:40:11	2180	2663043	0	False	3780	True
189	90420314	ActuallyNPH	Neil Patrick Harris	2009-11-16 15:54:48	343	25724729	163	False	51957	True
190	47869696	MindyWeiss	Mindy Weiss	2009-06-17 05:41:35	137	91823	114	False	1167	True
191	10399532	wipeyourstears	𝚐𝚊𝚋𝚛𝚒𝚎𝚕 🧣	2007-11-20 01:37:20	4512	10491	346	False	14	False
192	35787166	NICKIMINAJ	Nicki Minaj	2009-04-27 16:36:43	89928	23217212	5718	False	57884	True
193	25253190	RickHenryLA	Rick Henry	2009-03-19 06:58:37	691	18617	450	False	46	False
194	6326302	Geli	THE GELI	2007-05-25 20:41:11	4521	23278	951	False	104	False
195	19055236	karlihenriquez	Karli	2009-01-16 04:48:09	10898	103249	910	False	468	True
196	33160126	jcdecastelbajac	de castelbajac	2009-04-19 08:05:38	140	24308	299	False	275	False
197	59637063	ToddDelano143	Todd Delano	2009-07-24 00:25:02	391	15072	178	False	43	False
198	3004231	SnoopDogg	Snoop Dogg	2007-03-30 19:05:42	3106	19366036	2704	False	40685	True
199	25937606	formichetti	nicola formichetti	2009-03-23 02:42:25	1854	202994	6368	False	1869	True
200	115485051	ConanOBrien	Conan O'Brien	2010-02-18 20:17:16	1	28469838	1	False	77455	True
201	36890809	BonnieMcKee	Bonnie McKee	2009-05-01 05:23:09	10368	186485	7941	False	966	True
202	28706024	Pink	P!nk	2009-04-04 01:16:34	879	31615763	469	False	58932	True
203	79293791	rihanna	Rihanna	2009-10-02 21:37:33	1006	103645745	1002	False	93939	True
204	40415424	JohnnyWujek	JOHNNY WUJEK	2009-05-16 05:16:56	13	75835	420	False	309	False
205	26003792	RealMattLucas	Matt Lucas	2009-03-23 13:52:57	86509	1223779	11189	False	5017	True
206	27930140	benjimadden	Benjamin madden	2009-03-31 18:28:53	5947	462962	489	False	3952	True
207	24906019	yelle	YELLE Ⓨ	2009-03-17 16:19:51	21281	172226	164	False	1191	True
208	24872045	logandoesntweet	J. Logan Horne	2009-03-17 12:09:08	755	22842	947	False	98	False
209	32917485	ITSJEREMYSCOTT	JEREMY SCOTT	2009-04-18 15:40:47	3009	314556	169	False	0	True
210	23859156	MIAuniverse	M.I.A.	2009-03-12 00:16:22	551	620253	120	False	7649	True
211	18994204	thecobrasnake	mark hunter	2009-01-14 20:37:18	308	40195	414	False	723	False
212	19902330	robynkonichiwa	Robyn	2009-02-02 11:25:50	249	358739	305	False	3404	True
213	54930650	florencemachine	florence welch	2009-07-08 15:37:36	104	1016833	199	False	5146	True
214	24825539	greg_wells	Greg Wells	2009-03-17 02:43:42	178	26705	145	False	244	False
215	28507287	phillipeblond	Phillipe Blond	2009-04-03 04:58:35	732	22161	2147	False	258	True
216	34381602	matthewthiessen	Matthew Thiessen	2009-04-22 20:10:08	1831	61651	261	False	975	True
217	14085315	imyourpresent	Kelly	2008-03-05 21:57:52	32	17625	62	False	154	False
218	32211060	TheDoctorLuke	Dr. Luke Doctor Luke	2009-04-16 23:56:20	1768	194432	139	False	1096	True
219	23083434	ItsBennyBlanco	benny blanco	2009-03-06 16:26:08	12012	142585	308	False	507	True
220	26090319	KeshaRose	kesha	2009-03-23 21:07:57	278	3206196	1385	False	0	True
221	46576295	adamlambert	ADAM LAMBERT	2009-06-12 04:03:30	2022	2336016	1371	False	21387	True
222	28144053	JadynDouglas	JADYN	2009-04-01 17:02:08	140	26004	66	False	237	True
223	1374581	JungleJulia	Julia Ervin	2007-03-17 21:04:56	1591	17095	248	False	63	False
224	25023809	miamoretti	mia moretti	2009-03-18 03:55:22	2167	47301	686	False	272	True
225	18009781	joshgroban	josh groban	2008-12-10 02:29:32	18908	847690	937	False	9041	True
226	24296476	mikasounds	MIKA	2009-03-14 00:57:47	2810	1302912	29	False	5553	True
227	24407244	DitaVonTeese	Dita Von Teese	2009-03-14 18:24:31	8356	1725661	936	False	11583	True
228	30364057	SarahKSilverman	Sarah Silverman	2009-04-11 01:28:47	45496	12283623	1764	False	50937	True
229	19051166	JoJoWright	JoJo Wright	2009-01-16 02:34:08	28961	293508	7983	False	0	True
230	26116556	MDMOLINARI	MARKUS MOLINARI	2009-03-23 23:11:41	268	95057	1540	False	484	True
231	21355920	ferras	FERRAS	2009-02-20 00:10:50	586	60208	398	False	434	True
232	18838558	shannonwoodward	Shannon Woodward	2009-01-10 16:35:38	56627	224537	2551	False	974	True
233	14230524	ladygaga	Lady Gaga	2008-03-26 22:37:48	2321	83928213	118849	True	204269	True
234	19329393	PerezHilton	Perez Hilton	2009-01-22 06:47:48	197160	5533067	559	False	34670	True
235	17919972	taylorswift13	Taylor Swift	2008-12-06 10:10:54	1415	89436755	0	False	113638	True
236	15858184	whatdotcd	Trev🙃r	2008-08-15 01:14:05	23568	72537	4855	False	1039	False

	ID	Screen_Name	Name	Created_At	Favourites_Count	Followers_Count	Following_Count	Following	Listed_Count	Verified
0	3277891994	KingJosiah54	Josiah Johnson	2015-07-12 23:05:00	175368	196378	4434	False	1098	True
1	1315468723889811456	Lobos1707	Lobos Tequila 1707	2020-10-12 01:46:09	973	6598	48	False	9	False
2	1273305944902873089	morethanavote	More Than A Vote	2020-06-17 17:26:15	817	52476	85	False	287	True
3	73021361	tkelce	Travis Kelce	2009-09-10 02:35:54	3815	653079	1304	False	965	True
4	353178677	PatrickMahomes	Patrick Mahomes II	2011-08-11 17:31:20	14058	2016727	488	False	1868	True
5	14982804	ava	Ava DuVernay	2008-06-02 17:53:54	206304	2732206	16004	False	5905	True
6	1034198922888007680	spacejammovie	Space Jam: A New Legacy	2018-08-27 22:00:12	687	54144	39	False	71	True
7	54438929	JaredDudley619	Jared Dudley	2009-07-07 03:40:32	1129	370665	2033	False	3517	True
8	11347122	GavinNewsom	Gavin Newsom	2007-12-19 19:53:42	5372	1984831	22603	False	9388	True
9	1095095294033829888	Rjeff24	Richard Jefferson	2019-02-11 23:00:38	611	103103	16	False	466	True
10	17137891	Walmart	Walmart	2008-11-03 19:38:44	12864	1272444	360	False	5896	True
11	97752150	JimmySpencer	Jimmy Spencer	2009-12-18 21:31:55	19345	31780	5478	False	529	True
12	810889283062075392	rod_strickland	Rod Strickland	2016-12-19 16:47:25	10216	18179	1352	False	105	True
13	272116860	channingfrye	Channing Frye	2011-03-25 21:35:52	7438	139122	1089	False	1188	True
14	29952857	LaurenLondon	Lauren London	2009-04-09 09:29:42	4	2578830	212	False	6529	True
15	1054856541248008193	MillionDollar	Million Dollar Mile	2018-10-23 22:06:12	298	2217	12	False	6	True
16	3383587455	Zionwilliamson	Zion Williamson	2015-07-19 21:27:26	2438	476220	324	False	1238	True
17	92367751	jessicaalba	Jessica Alba	2009-11-24 21:17:31	2637	8670600	1717	False	24322	True
18	3111553475	halleberry	Halle Berry	2015-03-24 20:43:31	1192	510626	389	False	1060	True
19	22657202	Candace_Parker	Candace Parker	2009-03-03 18:37:41	440	379550	854	False	2563	True
20	1019672184929902592	IPROMISESchool	I PROMISE School	2018-07-18 19:56:08	2129	83211	137	False	151	True
21	164557288	B_Ingram13	Brandon X. Ingram	2010-07-09 04:36:49	552	344063	280	False	1316	True
22	909102066	ZO2_	Lonzo Ball	2012-10-27 23:18:46	5115	1268258	309	False	1934	True
23	318095329	joshhart	Josh Hart	2011-06-15 23:37:18	758	494031	386	False	1217	True
24	272616327	kylekuzma	kuz	2011-03-26 21:02:32	4203	1111587	29	False	1977	True
25	248900032	MagicJohnson	Earvin Magic Johnson	2011-02-07 23:29:07	129	5167194	222	False	13830	True
26	34862549	JeanieBuss	Jeanie Buss	2009-04-24 05:58:47	6697	405026	2294	False	3163	True
27	20346956	Lakers	Los Angeles Lakers	2009-02-08 02:14:35	12012	10341267	75	False	22495	True
28	1198406491	ReeseW	Reese Witherspoon	2013-02-19 19:22:38	9406	2961539	716	False	4320	True
29	1063034450	BenSimmons25	Ben Simmons	2013-01-05 13:43:35	4582	1031310	249	False	2125	True
30	36972997	NMAAHC	Smithsonian NMAAHC	2009-05-01 15:57:50	18681	230182	1001	False	2126	True
31	29403387	michaelb4jordan	Michael B. Jordan	2009-04-07 07:08:47	65	1349346	498	False	2292	True
32	4837832648	rodneyhood	Rodney Hood	2016-01-31 02:44:16	68	47736	677	False	307	False
33	28574769	heydb	Doris Burke	2009-04-03 14:36:55	1124	201476	1719	False	1792	True
34	154280902	Zendaya	Zendaya	2010-06-10 21:08:45	29408	19435694	1713	False	13438	True
35	59297233	mconley11	Mike Conley	2009-07-23 00:01:20	2688	534890	290	False	1948	True
36	26565946	jtimberlake	Justin Timberlake	2009-03-25 19:10:50	1286	63324954	326	False	67889	True
37	79293791	rihanna	Rihanna	2009-10-02 21:37:33	1006	103645745	1002	False	93939	True
38	784419106242125825	timelesssports_	Timeless Sports	2016-10-07 15:44:23	2208	196149	125	False	595	False
39	46422645	CassidyHubbarth	Cassidy Hubbarth	2009-06-11 16:19:07	28968	153420	2160	False	1778	True
40	28223714	bellachef19	Dena Marino	2009-04-01 23:18:15	2389	4737	2467	False	99	True
41	813286	BarackObama	Barack Obama	2007-03-05 22:08:25	0	130303149	588000	False	222355	True
42	19028953	JColeNC	J. Cole	2009-01-15 17:08:04	229	14137849	414	False	12665	True
43	17237299	LittleTikes	Little Tikes	2008-11-07 19:49:11	1023	37995	920	False	260	True
44	3884240178	NBCTheWall	The Wall	2015-10-13 20:39:29	1232	8578	211	False	34	True
45	23560015	warnerbros	Warner Bros.	2009-03-10 05:16:02	995	2400865	285	False	8150	True
46	23990483	WBHomeEnt	Warner Bros.	2009-03-12 17:42:27	2639	1295944	777	False	3807	True
47	59889953	Verizon	Verizon	2009-07-24 21:13:55	24391	1655128	683	False	5627	True
48	26589987	serenawilliams	Serena Williams	2009-03-25 21:00:33	610	10706300	369	False	21839	True
49	189033619	J1Flight	JC	2010-09-10 05:46:16	958	18463	244	False	247	True
50	188621689	matthewdelly	Matthew Dellavedova	2010-09-09 04:52:16	4564	177103	1404	False	842	True
51	23561980	kendricklamar	Kendrick Lamar	2009-03-10 05:44:26	17	11581895	507	False	10904	True
52	21701757	GQMagazine	GQ Magazine	2009-02-23 22:27:40	15149	1336297	874	False	7961	True
53	3837667054	TheSpringHillCo	The SpringHill Co	2015-10-01 22:57:31	11077	24897	268	False	87	True
54	609476852	De11eDonne	Elena Delle Donne	2012-06-15 22:17:17	6645	269282	676	False	0	True
55	131948686	JJWatt	JJ Watt	2010-04-11 21:01:08	4623	5632263	594	False	4279	True
56	459544233	BlazePizza	Blaze Pizza	2012-01-09 19:54:57	260339	73435	16027	False	360	True
57	2538115178	vanessabayer	Vanessa Bayer	2014-05-31 19:46:32	4889	168104	676	False	763	True
58	180505807	instagram	Instagram	2010-08-19 20:11:11	1117	34149031	251	False	0	True
59	1007506423	KendrickPerkins	Kendrick Perkins	2012-12-12 22:56:03	15098	489318	2175	False	1874	True
60	2282543629	TrainwreckMovie	#Trainwreck	2014-01-08 19:16:07	302	11120	22	False	69	True
61	306041929	Gibbsyyyy	Nicole Gibbs	2011-05-27 05:29:18	17796	31638	543	False	434	True
62	125402571	CoachDambrot	Keith Dambrot	2010-03-22 18:00:31	170	7806	96	False	46	True
63	2966774301	uninterrupted	UNINTERRUPTED	2015-01-07 22:18:06	7563	469813	765	False	790	True
64	19548850	TeamVic	Victor Cruz	2009-01-26 18:34:05	140	577180	849	False	2453	True
65	1059194370	kobebryant	Kobe Bryant	2013-01-04 01:09:40	313	15061491	515	False	16568	True
66	41160983	TheMikeOMalley	Mike O'Malley	2009-05-19 17:21:47	20898	49897	2207	False	327	True
67	1546274162	SRemorse_STARZ	Survivor's Remorse	2013-06-25 17:36:44	2629	39740	421	False	87	True
68	1705202990	CoachUrbanMeyer	Urban Meyer	2013-08-27 17:27:34	826	1921242	268	False	1432	True
69	55117855	amyschumer	Amy Schumer	2009-07-09 02:45:03	10395	4441326	1942	False	11428	True
70	72961879	KristenLedlow	Kristen Ledlow	2009-09-09 21:56:00	20848	143805	1116	False	1335	True
71	18618769	Jim_ICE	Jim Ice	2009-01-05 01:12:58	885	16004	565	False	94	True
72	18159470	macklemore	Macklemore	2008-12-16 09:31:07	972	2263857	1382	False	4421	True
73	848666563	KlutchSports	Klutch Sports Group	2012-09-27 04:38:56	813	71931	177	False	302	True
74	74791999	bubbawatson	bubba watson	2009-09-16 17:55:47	857	1901911	671	False	8267	True
75	1227787021	CoachDruJoyce	Dru Joyce II	2013-02-28 15:25:52	132	8909	94	False	19	False
76	16170744	AstronautKI	Clicquot Rico	2008-09-07 16:23:05	270	8214	555	False	19	False
77	255657736	StuartScott	Stuart Scott	2011-02-21 20:18:33	850	448746	663	False	2836	True
78	267425142	Dame_Lillard	Damian Lillard	2011-03-16 22:59:04	5078	2928791	2499	False	5078	True
79	349719397	TheRealJRSmith	JR Smith	2011-08-06 15:43:16	162	789478	1852	False	0	True
80	850183488	STVMHoops	STVM Hoops	2012-09-28 00:25:55	34	4323	158	False	8	False
81	183755853	DWRIGHTWAY1	Dorell Wright 🏁	2010-08-27 20:35:37	9226	122471	768	False	899	True
82	550483516	LJFamFoundation	LeBron James Family Foundation	2012-04-10 22:05:27	2506	79744	528	False	247	True
83	575513182	stvmirish	St. Vincent-St. Mary High School	2012-05-09 16:53:54	3956	12986	663	False	54	False
84	460417084	SianCotton85	SianCotton	2012-01-10 18:52:37	30	9242	2638	False	20	False
85	475458563	LeBronTeam	LeBron James Fans	2012-01-27 02:40:33	15	165481	135	False	214	False
86	230002598	DCarter12_	Darius Carter Jr.	2010-12-24 00:03:35	1543	13651	636	False	28	True
87	53643297	Yg_Trece	Paul George	2009-07-04 10:26:19	60	2386868	602	False	4656	True
88	24007253	CuffsTheLegend	73-9 and they LIED 🏁	2009-03-12 19:04:22	1321	58301	7959	False	572	False
89	217160945	kevinlove	Kevin Love	2010-11-18 19:10:05	1833	3184787	583	False	6963	True
90	416006433	dzanderTraining	David Alexander	2011-11-19 03:26:51	290	8443	160	False	42	False
91	371539066	ShannonSharpe	shannon sharpe	2011-09-11 01:57:28	3355	1559236	668	False	3804	True
92	502281810	OfficialJLD	Julia Louis-Dreyfus	2012-02-25 00:17:17	3192	1325061	597	False	4819	True
93	415859364	Nike	Nike	2011-11-18 22:31:18	7000	8863790	118	False	11155	True
94	277596528	HannahStormESPN	Hannah Storm	2011-04-05 17:55:02	12158	222799	1868	False	1683	True
95	120575218	UNKNWN	UNKNWN	2010-03-06 21:50:48	649	20825	35	False	111	False
96	94457225	ShaneBattier	Shane Battier	2009-12-04 01:10:34	289	429929	38	False	2336	True
97	421294708	WORLDWAD	Jawad Williams	2011-11-25 19:20:25	4230	19790	520	False	188	True
98	397216227	MikeMancias1	Mike Mancias	2011-10-24 11:53:08	562	7873	244	False	51	False
99	17523013	kingchip	Chip Tha Ripper	2008-11-20 21:32:12	42701	105293	6755	False	807	True
100	388014562	ThisIsUD	Udonis Haslem	2011-10-10 01:35:14	773	401764	579	False	1184	True
101	39288259	LarryFitzgerald	Larry Fitzgerald	2009-05-11 16:51:30	858	2200348	118	False	10163	True
102	379789292	Jada_AP	Jada Paul	2011-09-25 15:26:01	1	11831	229	False	32	False
103	25148455	a_dambrot	ADambrot	2009-03-18 20:36:17	14422	4512	964	False	8	False
104	377723724	JCrossover	🏁 Jamal Crawford	2011-09-22 00:55:53	85638	1562475	3683	False	3244	True
105	50811932	JHarden13	James Harden	2009-06-25 23:23:37	192	7285121	265	False	10089	True
106	25113646	MikeMiller_13	Mike Miller	2009-03-18 17:35:26	1011	429649	430	False	1895	True
107	17783653	jerreau	JERREAU	2008-12-01 17:25:54	4884	16387	651	False	0	True
108	333102754	pg30_Cole	Norris Cole II	2011-07-11 00:17:32	2196	273259	0	False	797	True
109	329187705	MDSB_Cartel	DaiRan Cartel	2011-07-04 17:33:58	1814	3076	328	False	4	False
110	56871360	SteveStoute	Steve Stoute	2009-07-15 00:39:08	26	90747	82	False	638	True
111	120677444	pr_RWTW	PR	2010-03-07 05:52:41	9137	15727	2216	False	80	True
112	66816759	RealTristan13	Tristan Thompson	2009-08-18 22:03:30	1	662540	272	False	0	True
113	347441185	D19J	Damon Jones	2011-08-02 20:12:32	1177	16769	399	False	127	True
114	345416858	Rafi_Jaffet	Rafiski	2011-07-30 15:24:32	275	3129	24	False	13	False
115	18943685	DonteWhitner	DonteHitner31	2009-01-13 16:50:57	285	130542	681	False	1295	True
116	334139724	MrsTru120	Trulynn	2011-07-12 16:52:06	46	2104	224	False	5	False
117	159113258	AntDavis23	Anthony Davis	2010-06-24 13:49:18	110	2187034	301	False	4728	True
118	65815940	mseweezy	ⓔⓡⓘⓒⓚⓐ	2009-08-15 03:38:31	267	3295	657	False	12	False
119	148377946	DaRealShow	Show	2010-05-26 15:02:28	1274	5856	839	False	17	False
120	28585586	BGCA_Clubs	Boys & Girls Clubs	2009-04-03 15:31:00	9777	48654	1309	False	729	False
121	41509142	Rachel__Nichols	Rachel Nichols	2009-05-21 02:20:44	38548	1182561	1074	False	12531	True
122	5885732	nikebasketball	Nike Basketball	2007-05-09 03:45:59	3584	2540221	231	False	4814	True
123	284089166	RealMikeWilbon	Michael Wilbon	2011-04-18 16:09:24	53	4800829	144	False	10633	True
124	19583545	LFC	Liverpool FC	2009-01-27 09:05:21	15489	18595689	362321	True	26196	True
125	251352182	MsDeidre	Deidre Norris	2011-02-12 23:46:23	43	2145	400	False	3	False
126	98435280	MikeWilliamsthb	Michael Williams	2009-12-21 18:03:34	32	3220	336	False	7	False
127	31212170	BrysonAdair	Bryson Jenkins	2009-04-14 20:09:09	1496	5655	615	False	8	False
128	14423603	beatsbydre	Beats by Dre	2008-04-17 17:53:30	1093	915628	303	False	2168	True
129	288956125	mavcarter	Maverick Carter	2011-04-27 19:10:00	37	86501	171	False	424	True
130	70835770	EJ_Fletch	Calmly_Inappropriate	2009-09-02 00:42:12	3	3142	338	False	12	False
131	141995679	313MannyHarris	Manny Harris	2010-05-09 16:47:07	255	20483	157	False	352	True
132	15846407	TheEllenShow	Ellen DeGeneres	2008-08-14 03:50:42	4417	77733357	26514	False	96862	True
133	130997057	AaronRodgers12	Aaron Rodgers	2010-04-08 23:54:25	3186	4524901	552	False	10432	True
134	28481943	NIKETyson	Slick Van Exel	2009-04-03 02:31:56	82	2078	107	False	8	False
135	190849080	_Cee1	CRS	2010-09-15 00:18:20	6596	4443	1454	False	9	False
136	225298897	SavannahRB	Savannah Brinson	2010-12-11 07:01:20	0	54439	175	False	198	True
137	195450194	MKG14	Mike Kidd-Gilchrist	2010-09-26 18:52:01	43	302445	210	False	1164	True
138	174533632	DezBryant	Dez Bryant	2010-08-04 04:30:48	6317	3480017	6736	False	4518	True
139	17929027	Wale	Wale	2008-12-06 21:15:10	5335	6358614	2604	False	10816	True
140	34430522	blakegriffin23	Blake Griffin	2009-04-22 22:51:03	814	4495522	567	False	11501	True
141	209233393	DesmondHoward	Desmond Howard	2010-10-28 19:47:32	613	381470	315	False	3027	True
142	17169320	common	COMMON	2008-11-04 21:18:21	3307	5419610	882	False	13101	True
143	98576365	EBled2	Eric Bledsoe	2009-12-22 07:18:55	54	236449	623	False	1508	True
144	42562446	StephenCurry30	Stephen Curry	2009-05-26 04:15:37	922	15642961	833	False	15189	True
145	207756999	teamlebron	Team LeBron	2010-10-25 23:59:38	4854	170815	1905	False	641	True
146	19923144	NBA	NBA	2009-02-02 19:04:42	2591	35301318	1886	False	52349	True
147	26371476	ahsenat	Nesh	2009-03-25 00:23:13	92	3533	472	False	15	False
148	179149890	RealJasonKidd	Jason Kidd	2010-08-16 16:41:43	107	562803	532	False	3219	True
149	56130390	Marcus32Johnson	MJ	2009-07-12 16:38:32	8	6418	853	False	40	False
150	18915240	JalenRose	Jalen Rose	2009-01-12 20:59:04	1717	1799495	877	False	10837	True
151	18791080	ChrysaChin	Chrysa Chin	2009-01-09 03:47:42	1317	20469	1073	False	79	False
152	15537451	SLAMonline	SLAM	2008-07-22 21:18:12	34125	1205650	20619	False	5712	True
153	190094605	ErnieRamos32	Ernie	2010-09-13 02:05:23	4	5073	332	False	22	False
154	178952994	BJwhite	BJ	2010-08-16 03:26:02	24101	3041	715	False	14	False
155	56386982	STVMAthletics	STVM Athletics	2009-07-13 14:33:41	4992	9486	851	False	53	False
156	20416406	SteveNash	Steve Nash	2009-02-09 04:44:08	651	2586143	1164	False	15578	True
157	57558113	mchalmers15	Mario Chalmers	2009-07-17 05:03:00	13	897551	478	False	3043	True
158	18610936	BunBTrillOG	Bun B	2009-01-04 20:41:22	116	1082738	1416	False	3516	True
159	171165627	NBAHistory	NBA History	2010-07-26 18:29:04	74	1564931	57	False	6346	True
160	21278412	myfabolouslife	Fabolous	2009-02-19 05:19:42	227	4123304	335	False	11116	True
161	188001657	LloydPierceLP	LP	2010-09-07 17:57:38	3086	7441	378	False	75	False
162	15802070	djmeel	Meel	2008-08-10 22:31:29	1971	14340	992	False	104	False
163	61297446	CamKnows_	Cam Joyce	2009-07-29 21:13:20	7551	6140	1127	False	27	False
164	35982046	russwest44	Russell Westbrook	2009-04-28 03:59:26	25	6822550	701	False	10075	True
165	42384760	carmeloanthony	Carmelo Anthony	2009-05-25 10:30:34	103	9252697	386	False	17047	True
166	132389474	JohnWall	John Wall	2010-04-13 03:12:46	2885	1912968	2854	False	6103	True
167	122547185	mister1223	philip h.	2010-03-13 02:50:55	147	3523	247	False	10	False
168	16136553	djstephfloss	DJ Steph Floss	2008-09-04 22:43:57	16329	71961	641	False	610	True
169	26609315	81smallz	RM (Rossi Mafia) Eddie	2009-03-25 22:22:46	5588	11976	1492	False	62	False
170	23151437	KevinHart4real	Kevin Hart	2009-03-07 02:02:31	131	36848300	555	False	22175	True
171	35936474	KDTrey5	Kevin Durant	2009-04-28 01:14:36	1163	19394015	1476	False	24454	True
172	21056862	BaronDavis	Baron Davis	2009-02-17 02:36:44	6721	1049391	8745	False	6059	True
173	176153263	ChillWill03	Willie D. McGee	2010-08-08 18:40:54	5194	5768	1607	False	16	False
174	54274783	fwalker03	Frankie Walker	2009-07-06 17:40:42	12	7747	197	False	24	False
175	132396074	BrandonWeems10	Brandon Weems	2010-04-13 03:39:28	188	17712	684	False	152	False
176	169679243	DruOnDemand	Coach Dru Joyce III	2010-07-22 22:39:06	3801	16636	818	False	60	False
177	145789715	RomeTrav	Romeo Travis	2010-05-19 22:02:32	33	33367	1456	False	106	True
178	23632429	RichPaul4	Rich Paul	2009-03-10 18:52:08	152	73082	657	False	265	True
179	27195114	Drake	Drizzy	2009-03-28 07:17:46	59	39356861	607	False	36770	True
180	33995409	DwyaneWade	DWade	2009-04-21 18:42:20	3425	9598445	814	False	21621	True
181	53853197	CP3	Chris Paul	2009-07-05 06:25:22	1430	8398535	930	False	14621	True

	ID	Screen_Name	Name	Created_At	Favourites_Count	Followers_Count	Following_Count	Following	Listed_Count	Verified
0	1450767861484507141	LiveScoreIE	Watch the UCL on LiveScore 🇮🇪	2021-10-20 10:16:38	38	929	70	False	1	True
1	212315904	livescore	LiveScore	2010-11-05 18:54:58	591	83059	318	False	169	True
2	1418438281230913540	zujugp	ZujuGP	2021-07-23 05:10:29	0	2517	1	False	3	False
3	2754030672	therabody	Therabody	2014-08-22 05:38:53	3222	11025	1144	False	49	False
4	956018303834775552	freefireth	Garena Free Fire TH	2018-01-24 04:18:20	211	31979	4	False	14	False
5	1054172393466486789	FreeFireBR	Leon na área 🏀	2018-10-22 00:47:38	655	927217	54	False	145	True
6	1008840241556393985	freefirelatino	Free Fire LATAM 🔥	2018-06-18 22:33:51	1616	598387	63	False	117	True
7	1202850204996665345	IndiaFreeFire	Free Fire India Official	2019-12-06 07:20:27	15	121530	0	False	19	True
8	1324677118660280320	SixpadHomeGym	SIXPAD HOME GYM【自宅が本格的なジムになる。先進のオンラインジム】	2020-11-06 11:37:00	4923	4022	30	False	1	False
9	4793420675	sixpadeurope	SIXPAD EUROPE	2016-01-13 10:53:52	655	11011	6	False	25	False
10	1085571299194355712	insparya_es	Insparya	2019-01-16 16:15:41	39	12112	14	False	13	False
11	216299334	piersmorgan	Piers Morgan	2010-11-16 09:37:44	9	7991918	1927	False	17306	True
12	259925559	DjokerNole	Novak Djokovic	2011-03-02 21:44:22	3141	8857780	759	False	16224	True
13	380730306	obj	Odell Beckham Jr	2011-09-27 04:02:10	255	4192353	1000	False	2778	True
14	155927976	rioferdy5	Rio Ferdinand	2010-06-15 14:47:40	3333	11235625	683	False	20491	True
15	19904728	MEOpt	MEO	2009-02-02 12:49:41	3785	37122	538	False	114	True
16	17711659	altice_portugal	@altice_portugal	2008-11-28 16:24:11	118	146730	109	False	260	True
17	743012834481692672	dazngroup	DAZN	2016-06-15 09:30:39	2254	99763	448	False	326	True
18	770304053637214208	CR7CrunchFitnes	CR7Crunch Fitness	2016-08-29 16:56:12	14	26843	189	False	22	False
19	714855921466359809	PestanaCR7	Pestana CR7	2016-03-29 16:45:07	405	33495	16	False	28	False
20	2865449969	tatelrestaurant	TATEL Restaurants	2014-11-07 10:46:14	3378	15069	474	False	70	False
21	253508662	juventusfc	JuventusFC	2011-02-17 11:34:21	3085	9589260	65	False	8851	True
22	994540152465821696	StrikerForce7	Striker Force 7	2018-05-10 11:30:24	10	21203	5	False	13	False
23	123682421	visitmadeira	visitmadeira	2010-03-16 22:24:09	10	15510	404	False	150	False
24	996714062636572672	polarissports	Polaris Sports	2018-05-16 11:28:45	190	30516	205	False	19	True
25	3282859598	TwitterVideo	Twitter Video	2015-07-18 00:54:11	8964	13736520	1	False	2684	True
26	2550023839	selecaoportugal	Portugal	2014-06-06 13:21:04	3199	1142539	283	False	2011	True
27	282985674	IndiaMartinez	India Martínez	2011-04-16 10:15:57	13556	620191	689	False	764	True
28	1178944580	JoaoMoutinho	João Moutinho	2013-02-14 12:48:24	2139	317138	53	False	585	True
29	58531272	FALCAO	Radamel Falcao	2009-07-20 17:23:57	300	17332359	631	False	12831	True
30	280701704	jamesdrodriguez	James Rodríguez	2011-04-11 21:45:31	263	19001871	292	False	11752	True
31	92245322	TAGHeuer	TAG Heuer	2009-11-24 10:49:05	10084	417293	134	False	1518	True
32	116362700	LilTunechi	Lil Wayne WEEZY F	2010-02-22 05:29:44	29	34849154	56	False	34238	True
33	11071292	Herbalife	Herbalife Nutrition	2007-12-12 01:37:10	4699	331895	530	False	880	True
34	42519612	FloydMayweather	Floyd Mayweather	2009-05-26 00:08:06	651	7882839	485	False	13185	True
35	49108476	yago	Dios Edge	2009-06-20 20:20:46	1672	37829	2274	False	119	False
36	79293791	rihanna	Rihanna	2009-10-02 21:37:33	1006	103645873	1002	False	93939	True
37	333534411	officialpepe	Pepe	2011-07-11 17:54:04	980	3539231	64	False	3248	True
38	33995409	DwyaneWade	DWade	2009-04-21 18:42:20	3425	9598445	814	False	21621	True
39	14055301	SavetheChildren	Save the Children US	2008-02-28 16:14:50	41754	2770462	4817	False	9028	True
40	27260086	justinbieber	Justin Bieber	2009-03-28 16:41:22	4678	114135591	286376	False	540730	True
41	344634424	RafaelNadal	Rafa Nadal	2011-07-29 10:44:02	277	15605048	137	False	22594	True
42	329789735	Fabio_Coentrao	Fabio Coentrao	2011-07-05 16:37:17	0	2316451	5	False	3439	True
43	155459701	BEAUcasperSMART	Beau Casper Smart	2010-06-14 05:42:07	1243	110172	47	False	180	True
44	85603854	JLo	jlo	2009-10-27 16:24:51	13074	45196998	1822	False	49750	True
45	180505807	instagram	Instagram	2010-08-19 20:11:11	1117	34149027	251	False	0	True
46	252753618	luisnani	Nani	2011-02-15 20:57:55	388	3893729	190	False	9619	True
47	56749149	RodolfoLanderos	Rodolfo Landeros	2009-07-14 17:00:37	20175	197198	3689	False	513	True
48	46452468	enriqueiglesias	Enrique Iglesias	2009-06-11 18:27:04	640	13865352	2544	False	15374	True
49	6446742	ufc	UFC	2007-05-30 16:11:00	18693	9034391	668	False	14446	True
50	156132825	MikeTyson	Mike Tyson	2010-06-16 03:10:23	585	5693149	405	False	13045	True
51	22922759	Mark_Sanchez	Mark Sanchez	2009-03-05 14:16:07	536	791984	274	False	6405	True
52	16801904	shaunwhite	Shaun White	2008-10-16 03:49:34	1118	1616401	53	False	8472	True
53	28669612	RealLamarOdom	LAMAR ODOM	2009-04-03 22:21:19	775	3659301	225	False	13250	True
54	183387449	XabiAlonso	Xabi Alonso	2010-08-26 21:54:09	1482	11675454	539	False	20885	True
55	34949757	aarbeloa17	Álvaro Arbeloa	2009-04-24 15:13:37	3670	5962184	1133	False	11319	True
56	14230524	ladygaga	Lady Gaga	2008-03-26 22:37:48	2321	83928286	118849	True	204269	True
57	60865434	KAKA	Kaka	2009-07-28 10:05:26	104	29308832	303	False	63666	True
58	40519997	ochocinco	Chad Johnson	2009-05-16 18:55:22	191	3268788	7205	False	23525	True
59	41147159	nikefootball	Nike Football	2009-05-19 16:16:20	1591	3754952	212	False	4947	True